Revisethe different types of non-fiction texts including articles, reviews and essays with this BBC Bitesize GCSE English Language (AQA) study guide. In linguistics, the term text refers to The original words of something written, printed, or spoken, in contrast to a summary or paraphrase. A coherent stretch of language that may be regarded as an object of critical analysis. Text linguistics refers to a form of discourse analysis—a method of studying written or spoken language—that is concerned with the description and analysis of extended texts those beyond the level of the single sentence. A text can be any example of written or spoken language, from something as complex as a book or legal document to something as simple as the body of an email or the words on the back of a cereal box. In the humanities, different fields of study concern themselves with different forms of texts. Literary theorists, for example, focus primarily on literary texts—novels, essays, stories, and poems. Legal scholars focus on legal texts such as laws, contracts, decrees, and regulations. Cultural theorists work with a wide variety of texts, including those that may not typically be the subject of studies, such as advertisements, signage, instruction manuals, and other ephemera. Text Definition Traditionally, a text is understood to be a piece of written or spoken material in its primary form as opposed to a paraphrase or summary. A text is any stretch of language that can be understood in context. It may be as simple as 1-2 words such as a stop sign or as complex as a novel. Any sequence of sentences that belong together can be considered a text. Text refers to content rather than form; for example, if you were talking about the text of "Don Quixote," you would be referring to the words in the book, not the physical book itself. Information related to a text, and often printed alongside it—such as an author's name, the publisher, the date of publication, etc.—is known as paratext. The idea of what constitutes a text has evolved over time. In recent years, the dynamics of technology—especially social media—have expanded the notion of the text to include symbols such as emoticons and emojis. A sociologist studying teenage communication, for example, might refer to texts that combine traditional language and graphic symbols. Texts and New Technologies The concept of the text is not a stable one. It is always changing as the technologies for publishing and disseminating texts evolve. In the past, texts were usually presented as printed matter in bound volumes such as pamphlets or books. Today, however, people are more likely to encounter texts in digital space, where the materials are becoming "more fluid," according to linguists David Barton and Carmen Lee " Texts can no longer be thought of as relatively fixed and stable. They are more fluid with the changing affordances of new media. In addition, they are becoming increasingly multimodal and interactive. Links between texts are complex online, and intertextuality is common in online texts as people draw upon and play with other texts available on the web." An example of such intertextuality can be found in any popular news story. An article in The New York Times, for example, may contain embedded tweets from Twitter, links to outside articles, or links to primary sources such as press releases or other documents. With a text such as this, it is sometimes difficult to describe what exactly is part of the text and what is not. An embedded tweet, for instance, may be essential to understanding the text around it—and therefore part of the text itself—but it is also its own independent text. On social media sites such as Facebook and Twitter, as well as blogs and Wikipedia, it is common to find such relationships between texts. Text linguistics is a field of study where texts are treated as communication systems. The analysis deals with stretches of language beyond the single sentence and focuses particularly on context, information that goes along with what is said and written. Context includes such things as the social relationship between two speakers or correspondents, the place where communication occurs, and non-verbal information such as body language. Linguists use this contextual information to describe the "socio-cultural environment" in which a text exists. Sources Barton, David, and Carmen Lee. "Language Online Investigating Digital Texts and Practices." Routledge, Ronald, and Michael McCarthy. "Cambridge Grammar of English." Cambridge University Press, Marvin K. L., et al. "Linguistic Perspectives on Literature." Routledge, 2015. Toconfigure the editing and proofing language: Within any Office application, select File > Options > Language. If your language already appears among the editing languages press Set as Default. Otherwise select the language from the Add additional editing languages list then press the Add button. With the increase in users of social media websites such as IMDb, a movie website, and the rise of publicly available data, opinion mining is more accessible than ever. In the research field of language understanding, categorization of movie reviews can be challenging because human language is complex, leading to scenarios where connotation words exist. Connotation words have a different meaning than their literal meanings. While representing a word, the context in which the word is used changes the semantics of words. In this research work, categorizing movie reviews with good F-Measure scores has been investigated with Word2Vec and three different aspects of proposed features have been inspected. First, psychological features are extracted from reviews positive emotion, negative emotion, anger, sadness, clout confidence level and dictionary words. Second, readablility features are extracted; the Automated Readability Index ARI, the Coleman Liau Index CLI and Word Count WC are calculated to measure the review’s understandability score and their impact on review classification performance is measured. Lastly, linguistic features are also extracted from reviews adjectives and adverbs. The Word2Vec model is trained on collecting 50,000 reviews related to movies. A self-trained Word2Vec model is used for the contextualized embedding of words into vectors with 50, 100, 150 and 300 pretrained Word2Vec model converts words into vectors with 150 and 300 dimensions. Traditional and advanced machine-learning ML algorithms are applied and evaluated according to performance measures accuracy, precision, recall and F-Measure. The results indicate Support Vector Machine SVM using self-trained Word2Vec achieved 86% F-Measure and using psychological, linguistic and readability features with concatenation of Word2Vec features SVM achieved may be subject to copyright. Discover the world's research25+ million members160+ million publication billion citationsJoin for free Citation Khan, Rizwan, A.;Faisal, Ahmad, T.; Khan, G. Identification of ReviewHelpfulness Using Novel Textual andLanguage-Context 2022,10, 3260. Editors Nebojsa Bacaninand Catalin StoeanReceived 15 August 2022Accepted 5 September 2022Published 7 September 2022Publisher’s Note MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional © 2022 by the MDPI, Basel, article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution CC BY license of Review Helpfulness Using Novel Textual andLanguage-Context FeaturesMuhammad Shehrayar Khan 1, Atif Rizwan 2, Muhammad Shahzad Faisal 1, Tahir Ahmad 1,Muhammad Saleem Khan 1and Ghada Atteia 3,*1Department of Computer Science, COMSATS University Islamabad, Attock Campus,Islamabad 43600, Pakistan2Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea3Department of Information Technology, College of Computer and Information Sciences, Princess Nourah BintAbdulrahman University, Box 84428, Riyadh 11671, Saudi Arabia*Correspondence geatteiaallah the increase in users of social media websites such as IMDb, a movie website, andthe rise of publicly available data, opinion mining is more accessible than ever. In the research fieldof language understanding, categorization of movie reviews can be challenging because humanlanguage is complex, leading to scenarios where connotation words exist. Connotation words havea different meaning than their literal meanings. While representing a word, the context in whichthe word is used changes the semantics of words. In this research work, categorizing movie reviewswith good F-Measure scores has been investigated with Word2Vec and three different aspects ofproposed features have been inspected. First, psychological features are extracted from reviewspositive emotion, negative emotion, anger, sadness, clout confidence level and dictionary readablility features are extracted; the Automated Readability Index ARI, the ColemanLiau Index CLI and Word Count WC are calculated to measure the review’s understandabilityscore and their impact on review classification performance is measured. Lastly, linguistic featuresare also extracted from reviews adjectives and adverbs. The Word2Vec model is trained on collecting50,000 reviews related to movies. A self-trained Word2Vec model is used for the contextualizedembedding of words into vectors with 50, 100, 150 and 300 pretrained Word2Vecmodel converts words into vectors with 150 and 300 dimensions. Traditional and advanced machine-learning ML algorithms are applied and evaluated according to performance measures accuracy,precision, recall and F-Measure. The results indicate Support Vector Machine SVM using self-trainedWord2Vec achieved 86% F-Measure and using psychological, linguistic and readability features withconcatenation of Word2Vec features SVM achieved neural network; Word2Vec; Natural Language Processing; sentiment classificationMSC 68T50; 68T071. IntroductionSentiment analysis is also known as opinion mining. The Natural Language ProcessingNLP technique is used to identify the sentiment polarity of textual data. It is one of thefamous research areas in NLP topics. People’s attitudes and thoughts about any movie,events or issue are analyzed with sentiment analysis of reviews. Sentiment analysis ofreviews classifies the review as having a positive or negative polarity that helps the userdecide about a product or any movie. While large volumes of opinion data can provide anin-depth understanding of overall sentiment, they require a lot of time to process. Not onlyis it time-consuming and challenging to review large quantities of texts, but some textsmight also be long and complex, expressing reasoning for different sentiments, making itchallenging to understand overall sentiment quickly once a new kind of communicationMathematics 2022,10, 3260. Mathematics 2022,10, 3260 2 of 20has been started between a customer and a service provider. People share their opinionabout services through websites. Usually, online products have thousands of reviews, andit is very difficult for the customers to read every review. Excessive and improper use ofsentiment in reviews makes them unclear concerning a product and it becomes difficult forcustomers to make the right decision. This entailed a Few-Shot Learner novel approachapplied for NLP tasks, including review sentiments, but focusing less on the impact ofinfluential textual features [1]. In this scenario, sentiment-based review classification isa challenging research problem. Sentiment analysis is a hot topic due to its applicationsquality improvement in products or services, recommendation systems, decision makingand marketing research [2]. The major contributions in the research are as follows•The proposed psychological features are positive emotion, negative emotion, anger,sadness, clout confidence level and dictionary words.•The readability features extracted according to Automated Readability Index ARI,Coleman Liau Index CLI and Word Count WC are calculated to measure thereview’s understandability score.• The linguistic features extracted are adjectives and adverbs.•The psychological, readability and linguistic features are concatenated with Word2Vecfeatures to train the machine-learning methods have been used to investigate data and convert raw data intovaluable data. One of the applications of computing is NLP [3,4]. Many advanced algo-rithms and novel approaches have improved sentiment classification performance, butmore productive results can be achieved if helpful textual reviews are used for sentimentclassification. New features are adverbs and adjectives in terms of sentiment classifica-tion [5,6], describing the author’s sentiments. The clout feature defines the confidence ofthe review written by the author. The review length feature determines the information thata review has and the readability feature defines how much information can be understoodor absorbed by the user. The readability feature also determines the complexity of anyreview for the reviews are short in length, representing opinions about products or a review given by a user has an important role in the promotion of a movie [7].Most people generally search for information about a movie on famous websites such asIMDb, a collection of thousands of movies that stores data about a movie’s crew, reviewsby different users, cast and ratings. Hence, surely it is not the only way to bring people tocinemas. In this regard, reviews also have an important analysis makes opinion summary in movie reviews easier by extractingsentiment given in the review by the reviewer [8]. Sentiment analysis of movie reviewsnormally includes preprocessing [9] and the feature-extraction method with appropriateselection [10], classification and evaluation of results. Preprocessing includes convertingall the capitalized words into lower-case words due to case sensitivity, stopping wordremoval and removing special characters that are preprocessed for classification. Differentfeature-extraction methods are used to extract features from the review of a movie orproduct [11]. Most feature-extraction methods are related to lexicon and statistical-basedapproaches. In statistical feature-extraction methods, the multiple words that exist inreviews represent a feature by measuring the different weighing calculations like InverseDocument Frequency IDF, Term Frequency TF and Term Frequency–Inverse DocumentFrequencyTF-IDF [12,13]. In the feature- extraction method lexicon, the extraction oftextual features from the pattern derived among the words is derived from the partsof speech of words tag [14]. The method based on lexicon extracts the semantics fromthe review by focusing on text ordering in sentiment analysis, short text and keywordclassification. The emotions using short text are written on social networking sites whichhave become popular. Emotions used in the review on social networking sites includeanxiety, happiness, fear, analysis of the IMDb movie review website finds the general perspectiveof review for emotions exhibited by a reviewer concerning a movie. Most researchers Mathematics 2022,10, 3260 3 of 20are working on differentiating positive and negative reviews. In the proposed work, acontextualized word-embedding technique is used Word2Vec. It is trained on fifty thousandreviews given by IMDb movie users. The qualitative features extracted using Word2Vecthat involves pretraining and the quantitative features are extracted from LIWC withoutpretraining. Experiments on vector features with different dimensions using the Skip-Gram Method are performed and LIWC extracts the quantitative linguistic features andpsychological features. The psychological features include positive emotion, negativeemotion, anger, sadness and clout, which measure confidence level from the reviews. Thereadability features include ARI, CLI and WC. Linguistic features include adjectives statistical and lexicon-based methods extract features to increase the model’saccuracy. When the features are extracted from the reviews, different feature selectiontechniques are applied to the features that help extract helpful features and eliminate thefeatures that do not contribute to the effectiveness of the classification of sentiment analysisof reviews [15,16]. The classification of sentiments of reviews defines the polarity of reviewsand classifies them as positive or negative. ML and lexical-based methods were used forsentiment analysis. ML methods have achieved high performance in academia as wellas in industry. It is a fact that ML algorithms make the classification performance able toachieve high performance, but data quality is important as well. Data quality can limit theperformance of any ML algorithm regardless of how much data are used to train the modelof the ML classifiers [17].2. Related WorkThere are two types of user reviews high-quality and low-quality. A high-qualityreview helps to participate in decision making, while a low-quality one reduces helpfulnessconcerning serving users. That is the reason it is necessary to consider the quality of reviewsfor large data identify the quality of reviews, many researchers consider high-quality reviewsand their helpfulness. Ordinal Logistic Regression OLR is applied to application reviewsfrom Amazon and Google Play with the feature of review length [18]. The Tobit regressionanalysis model has been applied to the dataset of TripAdvisor and Amazon book reviewsusing features of review length and word count [19]. The IMDb movie review dataset isselected for this research and serves as the dataset for sentiment classification. Multipletextual features are extracted using the Word2Vec model trained on reviews and LIWC inthis research helps to improve the classification performance of performance of sentiment analysis has been improved gradually with time byfocusing on advanced ML algorithms, novel approaches and DL algorithms. Details aregiven in brief in Table 1, describing the number of papers that achieved the best performanceconcerning review sentiments using advanced DL algorithm CNN-BLSTM was applied to the dataset of IMDb reviews andcompared with experiments on single CNN and BLSTM performance. In the dataset, wordswere converted into vectors and passed to the DL model [20]. Linear discriminant analysison Naive Bayes NB was implemented and achieved less accuracy using only thefeature of sentiwords [21].The Maximum Entropy algorithm was applied to the movie review dataset and fea-tures extracted by the hybrid feature-extraction method and achieved the highest compared to K Nearest Neighbor KNN and Naive Bayes NB. The features usedare just lexicon features positive word count and negative word count [22]. The highestaccuracy achieved for the IMDb dataset of online movie reviews was 89% because fewerdata were used 250 movie reviews concerning text documents for training purposes and100 movie reviews for testing purposes. Mathematics 2022,10, 3260 4 of 20Table 1. Summary of Accuracy achieved on the dataset of IMDb Models/ Approach Features Dataset Accuracy1CNNBLSTMCNN-BLSTMHybrid [20]Word embedding into vectors IMDb reviews Pre train model82% without the Pre train model2 LDA on Naive Bayes [21] Sentiword Net IMDb reviews Maximum Entropy [22] Sentiment words with TF IDF IMDb reviews Naive Bayes [23] Heterogeneous Features Movie review 89%5 Naive Bayes, KNN [2] Word vector sentiword Movie reviews Entailment as FewShot Learner [1] Word embedding into vectors IMDb reviews pretrainmodel7 Deep ConvolutionNeural Network [24] Vector Features IMDb Movie Reviews LSTM [25] Vector Features IMDb Movie Reviews Neural Network [26] Lexicon Features IMDb reviews 86%Heterogeneous features were extracted from the movie review to achieve the bestperformance for Naive Bayes [23]. There are also some other Amazon datasets publiclyavailable with many non-textual features. Furthermore, many researchers have also workedon an Amazon dataset, analysing reviews using non-textual features, which include productfeatures, user features and ratings [27,28]. The above literature concludes that to improvethe performance of the model features, the size of the dataset plays a more important role;only the use of an efficient algorithm is not sufficient to improve the performance of this experimentation dataset of 5331 positive and 5331 negative processed snippetsor sentences, the sentences are labelled according to their polarity. The total number ofsentences used for training purposes is 9595 sentences or snippets and 1067 sentences areused to test the model. First, the pretrained Word2Vec is used for feature extraction andthen Convolutional Neural Network CNN is applied to these features extracted fromWord2Vec. The Google News dataset contains 3 million words on which Word2Vec istrained to achieve the embedding of words into vectors. Testing accuracy is achieved onthe test dataset and is [24].In this paper, three datasets are used; the first dataset consists of 50 thousand reviews25 thousand are positive, and 25 thousand are negative. The data are already separated inthe form of training and testing reviews in which the ratio of positive and negative numbersof reviews is the same. The first drawback of this experimentation is that the dataset is notselected for training and testing of randomized models, which bringsbias to this paper. Thesecond dataset used in the experiments is 200 movies, each having ten categories in DoubanMovies. The rating of movies was from 0 to 5. A movie rating of 1 to 2 was considered anegative review and a 3 to 5 movie rating is considered a positive review of the movie. Thecomments that had a rating of 3 were ignored. So, there were 6000 used as training andthe other 6000 were used to test the dataset. The total number of comments achieved afterremoving neutral reviews was 12,000. The second drawback is that in this paper, the ratiois 5050 and most of the references show that 8020 or 7030 is the best ratio for splittingthe dataset. For evaluating the classification performance, three classifiers are used forsentiment classification. One is NB, an extreme learning machine and LSTM is conductedbefore that dataset is passed through Word2Vec for word embedding. The word vectorswere sent to LSTM for classification and the results show that LSTM performed better thanother classifiers. The LSTM F-Measure was [25]. The last reference mentioned in Mathematics 2022,10, 3260 5 of 20Table 1, shows that the accuracy achieved by NN is 86% using lexicon features. This alsoapplies to neural networks. In the IMDb dataset of movie reviews used in this research,reviews are normalized using the following steps All the words of reviews are convertedto lower case from upper case words or characters. Secondly, numbers are removed, specialcharacters, punctuational marks and other diacritics are removed. White spaces includedin the review were also removed. Finally, abbreviations are expanded and stop words inreviews are also removed. All the processing of reviews involved in the referred paper isdescribed above [26].Word Embedding Using the Word2Vec ApproachWhile representing a word, the context in which the word is used matters a lot becauseit changes the semantics of words. For example, consider the word ’bank’. One meaning ofthe word bank is a financial place, and another is land alongside water. If the word ’bank’is used in a sentence with words such as treasury, government, interest rates, money, etc.,we can understand by its context words its actual meaning. In contrast, if the context wordsare water, river, etc., the actual meaning in this case of context word is land. One of theemerging and best techniques we know for word embedding is used in many fields suchNLP, biosciences, image processing, etc., to denote text using different models. The resultsusing word embedding are shown in Table 2. Word2Vec results in other fields ResultsImage Processing [29] 90% accuracyNatural Language Processing Tasks [30] More than 90% accuracyRecommendation Tasks [31] Up to 95% accuracyBiosciences [32] More than 90% accuracySemantics Task [33] More than 90% accuracyMalware Detection Tasks [34] Up to 99% accuracyWord embedding is most important and efficient nowadays in terms of representing atext in vectors without losing its semantics. Word2Vec can capture the context of a word,semantic and syntactic similarity, relation with other words, etc. Word2Vec was presentedby Tomas Mikolov in 2013 at Google [35]. Word2Vec shows words in a vector space. Thewords in the review are represented in the form of vectors and placement is carried out sothat dissimilar words are located far away and similar meaning words appear together invector Proposed MethodologyThe proposed methodology, the environment of hardware and software was set asneeded to perform experiments. The hp laptop core i5 4th generation having 8 GB RAMis used for experimentation. The Google Colab software is used and is the IntegratedDevelopment Environment for the Python language in which we peformed our the latest libraries of Python are used for experiments. The research methodologyconsisted of four steps. The steps are dataset acquisition, feature engineering, models andevaluation, shown in Figure 1below. Figure 1defines that after preprocessing of dataacquisition from the IMDb movie review website, it is passed for feature engineering, whichconsists of three blocks B, C and D. B, C and D blocks are used independently as well asin hybrid; B and C, and B and D blocks are named Hybrid-1 and Hybrid-2, E consists of 10-fold cross-validation, training and testing of different ML modelsand the last one is the evaluation process of models. After extraction of features, eachfeature is normalized using the Min/Max Normalization technique. On the normalizedfeature, 10-fold cross-validation is applied to remove the bias. Machine-learning ML anddeep-learning DL models are trained and tested; these are Support Vector Machine SVM,Naive Bayes NB, Random Forest RF, Logistic Regression LR, Multi-Layer Perceptron Mathematics 2022,10, 3260 6 of 20MLP, Convolution Neural Network CNN and Bidirectional Gated Recurrent Unit Bi-GRU. The results were achieved after implementing models on features and were Review Dataset AcquisitionLinguistic Inquiry and Word CountWord2Vec model training and word Embedding Pretrained glove modelMin/Max Normalization Hybrid A+B Hybrid A+B10 k fold stratified cross validationSVMNBRandom ForestLogistic RegressionCNNBiGRUAccuracyRecallPrecisionF1 ScoreComparison AB C DEFigure 1. General Diagram of working flow of Research Dataset AcquisitionThe benchmark of the movie review dataset from IMDb is collected and availablepublicly. The main dataset exists of 50,000 reviews with polarity levels. The ground ratingis also available according to the 10-star rating from different customers. A review with arating of less than 4 is a negative review, and a review with a score of more than seven is apositive review. All the reviews are equally pre-divided into 25,000 positive reviews andthe other 25,000 negative. Each review is available in the text document. Fifty-thousandtext documents containing reviews were Preprocessing for Feature ExtractionAfter downloading, each text document including reviews is preprocessed by usingPYCHARM IDE. In two columns, all the reviews and their polarity are read and written inthe Comma Separated Value CSV file. One column indicates the reviews and the secondcolumn indicates the polarity. Firstly, the reviews in sentences tokenized into words andthen all the special characters, stop words and extra spaces are removed from the reviewusing the NLP tool kit library available in Python. The preprocess reviews are written upin the preprocess column of the CSV file for future Data Preprocessing ToolFor data preprocessing, we use the tool PYCHARM 2018 IDE and Python version Natural Language Tool Kit NLTK is used for text processing such as tokenizationand stop word removal. Google Colab is used for implementing DL algorithms because itprovides GPU and TPU for fast processing. Mathematics 2022,10, 3260 7 of Feature Feature Extraction Using LIWCThe LIWC consists of multiple dictionaries to analyze and extract the features. Toextract psychological, textual and linguistic features from the movie review dataset, LIWCis used. First, the reviews are preprocessed and then used to extract features, as describedin Figure 2. The diagram flow is defined as the preprocessed reviews passing sent to LIWCfor extraction of the feature. LIWC compares each word of review from its dictionariesto check which category the given review word belongs to. It calculates the percentageby counting the number of words in the review that belong to a specific category anddivides by the total number of reviews. The division result is multiplied by 100 to obtain apercentage as described in Equation 1.x=Count the number o f words in review that bel ong to s peci f ic categoryTotal numb er o f words i n review s ×100 1xdenotes the specific subcategory of features in LIWC. The features calculated byLIWC are positive emotionPE, negative emotionNE, angerAng, sadness, clout,dictionary wordsDic, adverbsAdvand adjectivesAdj.PE,NE,Ang,SadandCloutare categorized byLIWCas psychological categorized aslinguistic ReviewsLowercase ,Remove stop words, Extra spaces, special characters, LemmatizationLIWCExtract FeaturesLinguistic/Summary LanguagePsychologicalReadabilityEPositive Emotion, Negative Emotion, Anger, Sad, clout, Adjective, Adverb, Dictionary Words ARI, CLI, Word CountBFigure 2. Feature Engineering with 3shows that after the extraction of features, Min/Max Normalization is appliedand then passes through block E for further implementation, including 10-fold cross-validation, training of ML models, testing of ML models and last is evaluation. Mathematics 2022,10, 3260 8 of 20InmovieheroplaygoodWt Wt-2 Wt-1 Wt+1 Wt+2 Wt+3 Sci-fiction000100Input Layer Hidden Layer Output Layer Example In Sci-fiction movie hero play good role ever and the Window size 7Figure 3. General Diagram of working flow of Research Readability Feature ExtractionThe readability score of reviews defines the effort required to understand the text ofreviews. The three readability features are calculated on the preprocessed reviews ARI,CLI and word is used for measuring the readability of English text and it is calculated by usingthe formula given in Equation 2.ARI = ×CW+ ×WS− 2where Crepresents characters that counts letters and numbers in review, Wrepresentswords and the number of spaces in review. Srepresents sentences that is the number ofsentences in Iscores define how difficult text is to understand and it is calculated by using theformula given in Equation 3.CL I = 3whereLrepresents the average number of letters per 100 words and S represents theaverage number of sentences per 100 words to measure the understandability of a count WC is calculated by linguistic inquiry word count which consists ofmultiple dictionaries and is calculated with Equation 4.WordCount =Nallwords −N punctuation−Nstopwords −Nnonalpha 4where Nallwords represents the total number of words in the review text, Npunctuationrepresents the number of punctuation characters in the review text, Nstopwords representsthe number of stop words in the review text and Nnonalpha represents the number ofnon-alphabetic terms in the review the extraction, each readability feature of Min/Max Normalization is applied, asdescribed in the next Word Embedding by Review-Based Training of Word2Vec ModelThe features of movie reviews are extracted by training the Word2Vec neural sequence of the feature-extraction process is given in Figure 4below. Firstly, for Mathematics 2022,10, 3260 9 of 20training, the neural model of Word2Vec data is prepared using the dataset of IMDb moviereviews with 50 thousand reviews. The total number of words included in this dataset is6 review was used in the training of the Word2Vec neural model and three differentembedding sizes were used in experiments, 50, 100 and 150, with a context size of 10. Thereare two methods for training the Word2Vec neural model; one is the COBOW context ofthe bag of words and the second one is the Skip-Gram Method. We used the Skip-GramMethod, which focuses on less frequent words and gives good results concerning wordembeddings of less frequent words. Skip-Gram Method operations are given in Figure 3defines that the model is trained by defining the window size 10 and Skip-Gram computes word embedding. Instead of using context words as input to predict thecenter word like a context bag of words, it used the center word as input and predictsthe center word’s context words. For example, “In Sci-fiction movie hero play good role”with context size 7. Training instances are created such as “In” is the target word which isthe input and the context word “Sci-fiction movie hero play the good role” is the outputword. The training instances are given in Table 3. Using training samples defined above inthe table used for training the neural network, the result of word embedding is generatedfor each word given in the vocabulary. The trained model is saved and movie reviewspass to these models for converting words into vectors. Three different types of vectorshaving sizes of 50, 100 and 150 are created. For classification, Word2Vector features areused measured by Skip-Gram Method passed to block ReviewsLowercase ,Remove stop words, Extra spaces, special characters, Lemmatization50 Thousand Reviews6,142,469 wordsVocabularyTrain Word2vec Neural ModelTrained Word2vec ModelTrained Word2vec ModelTrained Word2vec Model50 embedding sizeContext size 10 100 embedding sizeContext size 10150 embedding sizeContext size 10Skip gram Method Skip gram Method Skip gram MethodTest Model Test Model Test ModelVector50 Vector100 Vector150EConvert Sentences into wordsC1CFigure 4. Feature Extraction Process with self Pretrained Word2Vec 3. Word2Vec Results in other fields’ OutputIn sci-fictionIn movieIn heroIn playIn goodIn roleIn ever Mathematics 2022,10, 3260 10 of Word Embedding by Pretrained Word2Vec ModelThe Glove Model is an unsupervised learning algorithm used for vector representationof words. Training samples are taken from Wikipedia and different books. The GloveModel uses a generalized kind of text on which it is trained. Figure 5describes the stepsfor word embedding into first step is to download the Glove Model in the zip file with 150 vectors and300 vectors. The pretrained Glove Model is loaded and passed for test on the preprocessedreviews. Each preprocess review consists of words and is passed to the test model as inputand output are received as the vector of each review by taking the average of vectors. Eachreview vectors has 150 and 300 numbers in review vectors. The output of the vector ispassed to the E block for further implementation, which includes 10-fold cross-validation,training ML models, testing ML models and Million Vocabulary Wikipedia +books Preprocessed ReviewsConvert Sentences into wordsTrained glove Model 300Trained glove Model 150Test trained ModelTest trained ModelVectors 150 Vectors 300ETaking Mean of vectors Taking Mean of vectors DFigure 5. Feature Extraction Process with Pretrained Word2Vec Evaluation and DatasetThe dataset selected for the experiment is IMDb movie reviews, consisting of 50,000 re-views of different movies with sentiment polarity. The reason for this dataset selection isthat it is the largest number of reviews compared to the previously uploaded dataset ofmovie reviews on the website accessedon 4 April 2022. A total of 25,000 reviews are positive and the other 25,000 thousandreviews are negative. Each review is in the text file so in the zip file 50,000 text files areincluded with their rating value from 1 to 10 as text filename. Mathematics 2022,10, 3260 11 of Feature Exploration and Hypothesis TestingIn this subsection, the linguistic, psychological and readability features extracted fromthe reviews and used in the sentiment-based review classification are explored. A summaryof the descriptive statistics of the features under each category linguistic, psychologicaland readability are provided in Table 4. This summary includes the number of datarecords N, mean, median, standard deviation SD, maximum Max and minimum Minvalues of the features under each category. Moreover, the significance of the featuresrelated to the three categories is examined using hypothesis testing. In order to select theright significance test, the normality of the features is examined. To obtain a sense of thedistributions of features and outcome variable, histograms and associated distributioncurves are plotted as depicted in Figure 6. It is noteworthy that only CLI has a well behavedbell-shaped distribution curve while all other features are not. To confirm this observation,normal probability plots for all features are provided in Figure 7. A normal probability plotdemonstrates the deviation of record distribution from normality. It has been observed thatthe Adv, Adj and Clout distributions deviate slightly from normal distribution. However,all other feature distributions except CLI are not normally investigate the association between input features, a correlation matrix is the probability distributions of most features are not Gaussian, it is not possible to usePearson correlation to check the relationship between features. In contrast, the Spearmancorrelation coefficient is an efficient tool to quantify the linear relationship between con-tinuous variables that are not normally distributed [36]. As this is the case of our inputfeatures, Spearman correlation has been adopted in this study to quantify the associationbetween the features. A heat map of the Spearman correlation coefficient is created andpresented in Figure 8. The circle’s size is indicative of the strength of bivariate correlation map of Figure 8reveals a strong relationship between anger and negativeemotions and between ARI and CLI features, and a moderate association between NE andsadness and between Dic and ARI and CLI. However, the map shows weaker associationbetween the other input features. As the outcome, polarity class, is a categorical variable,the correlation coefficient is not an adequate tool to measure its association with the inputfeatures. Therefore, Binomial Logistic Regression LR has been adopted to investigate thisassociation. Logistic Regression assesses the likelihood of an input feature being linked toa discrete target variable [37]. The input features do not exhibit high multicollinearity, asdeducted from the correlation matrix plot of Figure 8, which makes the LR a suitable test ofassociation for our problem. Table 5displays the output of a Binomial Logistic Regressionmodel that was fitted to predict the outcome based of the linguistic, psychological andreadability feature values. The p-values and significance levels for each of the regressionmodel’s coefficients are listed in Table 5. The asterisks denote the level of the feature’ssignificance; more asterisks imply a higher level of importance. If the associated p-valueis less than three asterisks are used to denote significance, two asterisks are used torepresent significance if the corresponding p-value is in the range [ one asteriskreflects a p-value between and and no asterisk is for p-values larger than Asshown in Table 5, the p-values for PE, NE, Ang, Sad, Clout, Adj and CLI indicate that thesefeatures are statistically significant to the polarity class. Mathematics 2022,10, 3260 12 of 20Figure and probability distribution curves for linguistic, physiological, readabilityfeatures and polarity class probability plots for linguistic, physiological, readability features and polarity classvariables. Mathematics 2022,10, 3260 13 of 20Table statistics summary of linguistic, psychological, readability features and NE Ang Sad Clout Dic Adv Adj WC ARI CLI PolarityMean 175 0 0 0 0 0 0 12 − − 0Max 99 1304 1N2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000Table significance of linguistic, psychological and readability features using BinomialLogistic S-Error t-Statistics p-Value QualityPE − − **NE ***Ang ***Sad *Clout ***Dic **WC − − ***Figure Correlation coefficient matrix of linguistic, psychological and readability Chi-square hypothesis test is conducted to verify the sufficiency of the LR modelto test a feature’s significance. The null hypothesis of the test, H0, assumes that there isno relationship between the response variable, the polarity and any of the input features, all model coefficients except the intercept are zero. On the other hand, the alternativehypothesis, H1, implies that if any of the predictor’s coefficients is not zero, then thelearning model is called efficient. The p-value of the Chi-square test of the model wasrecorded as 1988 degrees of freedom on 2000 observations for all indicates that the LR model differs statistically from a constant model with only theintercept term and can be considered as an adequate test of feature significance. As aresult, the null hypothesis can be rejected, and the association between the input featuresin predicting the polarity of a review is confirmed. As depicted in Table 4, the binomial LRreveals that all psychological features are significant. However, only Adj from the linguistic Mathematics 2022,10, 3260 14 of 20features and CLI from the readability features are significant. Therefore, only significantfeatures are used for review classification in this Evaluation Measure and Performance ComparisonThe evaluation of the deep-learning and conventional models is carried out by calcu-lating the performance measures accuracy, precision, recall and F-Measure. These perfor-mance measures are calculated on the basis of a confusion matrix. The details of confusionmatrixes are given Confusion MatrixA confusion matrix is also known as an error matrix and is used for measuring theperformance of a classification model. A confusion matrix is represented in Figure a review is an actual negative, and the model is predicted as positive it is calledfalse positive FP. When a review is an actual positive, and the model is predicted to bepositive, it is called true positive TP. When a review is an actual positive, and the model ispredicted as negative, it is called false negative FN. When a review is an actual negative,and the model is predicted as negative, it is called true negative TN.TP FPFN TNPositive1 Negative0Positive1Negative0Actual ValuesPredicted ValuesFigure 9. Confusion Pretrained Word EmbeddingThe pretrained word embedding Glove experimented with two different words em-bedding word vector dimensions 150 and 300. The 6 ML classifiers are used with 150 wordvector dimensions and each word vector is tested. The experiments with 150 and 300 wordvector dimension and their results are shown in Tables 6and 6. Results of pretrained model of vector dimension of 150 Accuracy Precision Recall F ScoreMulti-LayerPerceptron NearestNeighbor” Forest Bayes VectorMachine Mathematics 2022,10, 3260 15 of 20Table 7. Pretrained model of vector dimension 300 Training Accuracy Average Testing Accuracy AverageCNN the movie review dataset preprocessing, it is passed to the 10-fold stratified cross-validation for the unbiased splitting of the dataset. The Glove pretrained model for featureengineering process is used. The 150 dimensions of the Glove pretrained model are used asa feature for ML models. The six ML algorithms are applied and SVM achieves the bestresults concerning other algorithms NB, RF, LR, KNN and MLP on the evaluation measuresof accuracy, precision, recall and F-Measure. The highest F-Measure score achieved is SVM, which is the impact of the pretrained Glove Model with 150 dimensions offeature vectors. The ML algorithm performs better on the 150 dimension vector of MLP, three layers are used with 20 neurons at each layer to predict review impact of the pretrained Glove Model having 300 dimensions is represented inTable 7. The two DL models are applied to features having a vector dimension of used models are CNN and Bi-GRU and the best results are achieved with Bi-GRUwith testing accuracy. The lowest dimension of the pretrained model is 150, whichleaves a higher impact on the results using the traditional ML algorithm compared to the300 dimensions using the DL Review-Based Trained Word2Vec Model Word EmbeddingThe reviews are embedded into vectors with three different word vector size dimen-sions, 50, 100 and 150. Then, the ML and DL algorithms are applied to varying sizes ofvectors of 28 dimensions independently and evaluated. Finally, the results are shown inTable 8based on the 8. Trained Model on reviews with 50 word vector dimension evaluation Accuracy Precision Recall F ScoreNaive Bayes Forest VectorMachine 50 dimensions of the Word2Vec model are self-trained on movie reviews. Afterthat self-trained model, it is used for word embedding of the movie reviews into vectorsrepresenting the meaning of that word. Then, the six ML algorithms are applied. The SVMachieves the best results compared to other algorithms, NB, RF, LR, KNN and MLP, on theevaluation measures accuracy, precision, recall and F-Measure. The highest F Measure scoreachieved is using SVM with 50 word embedding dimension, which is the impact ofthe self-trained model with a smaller number of dimensions. In Table 9, the 100 dimensionparameter of the self-trained model is evaluated using a confusion matrix. Mathematics 2022,10, 3260 16 of 20Table 9. Without the pretrained model with a 100 word vector dimension evaluation Accuracy Precision Recall F ScoreNaive Bayes NearestNeighbor Forest VectorMachine 100 dimensions of the Word2Vec model are self-trained on movie reviews. Afterthat model is self-trained, it is used for word embedding of the movie reviews into vectorsrepresenting the meaning of that word. Then, the six ML algorithms are applied. The SVMachieves the best results compared to other algorithms, NB, RF, LR, KNN and MLP on theevaluation measures accuracy, precision, recall and F-Measure. The highest F-Measurescore achieved is using SVM with 100 word embedding dimensions, which is theimpact of the self-trained model with a higher number of dimensions than previous Table 10, the impact of 150 dimensions of the self-trained model is trained on reviews of 150 word vector dimension without psychological, linguisticand readability features evaluation Accuracy Precision Recall F ScoreNaive Bayes NearestNeighbor Forest VectorMachine 150 dimensions of the Word2Vec model are self-trained on movie reviews. First,the context size of the model is set to 10 and the Skip-Gram Method is used to train theWord2Vec model. After that model is self-trained, it is used for word embedding of themovie reviews into vectors representing the meaning of that word. Then, the six MLalgorithms are applied. The SVM achieves the best results compared to other algorithms,NB, RF, LR, KNN and MLP on the evaluation measures accuracy, precision, recall andF-Measure. The highest F-Measure score achieved is using SVM with 150 wordembedding dimensions, which is the impact of the self-trained model with a higher numberof dimensions than the previous 50 and 100 dimension results. In Table 11, the impactof 150 dimensions of the self-trained model in addition to psychological, linguistic andreadability features is defined. The 150 dimension self-trained model with proposedfeatures is considered because it shows better results than the pretrained Glove psychological features are extracted using LIWC. Next, the psychological featuresused in this experiment are positive emotion, negative emotion, anger, sadness, clout anddictionary words. CLI’s readability feature is used because it gave a better result in theprevious experiment. Mathematics 2022,10, 3260 17 of 20Table trained on reviews of 150 word vector dimension with psychological, linguistic andreadability features evaluation Accuracy Precision Recall F ScoreNaive Bayes NearestNeighbor Forest VectorMachine the six ML algorithms are applied. The SVM achieves the best results withrespect to other algorithms, NB, RF, LR, KNN and MLP, on the accuracy, precision, recall andF-Measure evaluation measures. The highest F-Measure score achieved is using psychological, linguistic and readability features improve the evaluation 12 shows the impact of 300 dimensions of the self-trained model concerning results on word embedding 150 word vectors with psychological and Training Average Accuracy Testing AccuracyCNN 2 Layers evaluation result of two DL algorithms applied to 300 dimension vectors withoutpsychological and readability features. The impact on accuracy of 300 dimensions of theself-trained model is higher than the 300 dimensions of the pretrained model. The resultsshow that the method of embedding that is context-based gives higher results with respectto global based embedding. The applied models are CNN with two layers with 32 and64 neurons, respectively. Bi-GRU is used, which has two gates; one is an updated gate andthe other is a reset gate. The update gate is used to the retain memory and the reset gateis used to forget memory. The best results are achieved with Bi-GRU with testingaccuracy as compared to the pretrained Glove evaluation results of two DL algorithms applied on 300 word vectors with psy-chological and readability features are given in Table results on word embedding 300 word vectors with psychological, linguistic andreadability Training Accuracy Average Testing Accuracy AverageCNN the psychological features are extracted using LIWC. The psychological featuresused in this experiment are positive emotion, negative emotion, anger, sadness, clout anddictionary words. CLI’s readability feature gave a better result in the previous applied models are CNN with two layers with 32 and 64 neurons, Bi-GRU has two gates; one is an updated gate and the other is a reset gate. The updatedgate is used to retain the memory and the reset gate is used to forget the memory. Bi-GRUachieves the best results with testing accuracy compared to the pretrained GloveModel. In Table 14, a comparison is given between the proposed work and the previouswork based on evaluation measures. Mathematics 2022,10, 3260 18 of 20Table 14. Comparison of F-Measure of Proposed work with Previous Embedding Model Classifier F-MeasureReview based trainedWord2Vec Support Vector Machine Word2Vec [16] CNN-BLSTM Word2Vec [22] LSTM [18] Maximum Entropy analysis of the results following the experiment is given below.•The self-trained Word2Vec model on movie reviews with 150 dimension parameterhas a higher impact on performance than the pretrained Glove Model.• The CLI readability achieved the highest score compared to ARI and WC.•The SVM algorithm performs better than the applied algorithms NB, LR, RF, CNN,KNN and MLP.•The use of the psychological and readability feature CLI to classify reviews withself-trained embedding improves the performance from 86% to smaller number of words embedding dimension 150 performs better concerningthe traditional ML algorithm and for the DL algorithm 300 dimensions gives a ConclusionsClassification of opinion mining of reviews is open research due to the continuousincrease in available data. Many approaches have been proposed to achieve classificationof movie reviews. After a critical analysis of the literature, we observe that words areconverted into vectors for sentiment classification of movie reviews by different approaches,including TF-IDF and Word2Vec. The pretrain model of Word2Vec is commonly used forword embedding into vectors. Mostly generalized data are used to train the Word2Vecmodel for extracting features from reviews. We extract features by training the Word2Vecmodel on specific data related to 50 thousand reviews. For review classification, theWord2Vec model is trained on reviews. Most researchers used a generalized trained modelfor review classification as an alternative. This research work extracts features from moviereviews using a review-based trained Word2Vec model and LIWC. The review-basedtrained data have some characteristics. They include 6 million vocabularies of the wordand are specific to movie reviews related to the task of sentiment classification of six ML algorithms are applied, and SVM achieves the best result of F-Measurewith respect to other algorithms NB, RF, LR, KNN and DL algorithms are also applied. One is CNN and the other is Bi-GRU. Bi-GRUachieved which is greater than the results CNN achieved. The results conclude thatthe data used for model training perform better than the model trained on generalized the ML algorithm,150 features perform better than 50 and 100 features for theused movie review dataset. The DL model 300 feature vectors perform better classificationsthan the 150 feature vectors. Significant psychological, linguistic and readability featuresaided in improving the classification performance of the used classifiers. SVM achievedan F-Measure with 150 word vector size and BiGRU achieved the same F-Measurescore using 300 word vector size. We applied both traditional ML and DL algorithmsfor the classification of reviews. Both achieved nearly the same results on a performancemeasure that proves that the dataset of IMDb movie reviews having 50,000 is not enoughfor applying a DL algorithm. In future work, a larger dataset is needed to apply the DLalgorithm to increase the classification performance of ContributionsConceptualization, Muhammad Shehrayar Khan, Saleem Khan, Muhammad Shehrayar Khan, and methodology, Muhammad Shehrayar Khan, Muhammad Saleem Khan, and Mathematics 2022,10, 3260 19 of software Muhammad Shehrayar Khan, Muhammad Saleem Khan, and validation, Muhammad Shehrayar Khan, MuhammadSaleem Khan, and formal analysis, Muhammad Shehrayar Khan, and investigation, Muhammad Shehrayar Khan, MuhammadSaleem Khan, and resources, Muhammad Shehrayar Khan; data curation, Saleem Khan; writing original draft preparation, Muhammad Shehrayar Khan, Muhammad Saleem Khan, and writing review and editing, Shehrayar Khan, Muhammad Saleem Khan, and Muhammad Shehrayar Khan, Muhammad Saleem Khan, and supervision, project administration, funding acquisition, and authors have read and agreed to the published version of the This research received no external Availability StatementI declare that the data considered for this research is original andcollected by the authors for generating insights. 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ResearchGate has not been able to resolve any citations for this healthcare agencies HHCAs provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with retrieval from huge social web data is a challenging task for conventional search engines. Recently, information filtering recommender systems may help to find movies, however, their services are limited because of not considering movie quality aspects in detail. Prediction of movies can be improved by using the characteristics of social web content about a movie such as social-quality, tag quality, and a temporal aspect. In this paper, we have proposed to utilize several features of social quality, user reputation and temporal features to predict popular or highly rated movies. Moreover, enhanced optimization-based voting classifier is proposed to improve the performance on proposed features. Voting classifier uses the knowledge of all the candidate classifiers but ignores the performance of the model on different classes. In the proposed model, weight is assigned to each model based on its performance for each class. For the optimal selection of weights for the candidate classifiers, Genetic Algorithm is used and the proposed model is called Genetic Algorithm Voting GA-V classifier. After labeling the suggested features by using a fixed threshold, several classifiers like Bayesian logistic regression, Naïve Bayes, BayesNet, Random Forest, SVM, Decision Tree, LSTM and AdaboostM1 are trained on MovieLens dataset to find high-quality/popular movies in different categories. All the traditional ML models are compared with GA-V in terms of precision, recall and F1 score. The results show the significance of the proposed features and proposed GA-V KhanSalabat KhanAtif Rizwan Nagwan AbdelsameeIt is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, its impact on society also increases, from primarily being positive to negative. Cyber aggression is a negative impact; it is defined as the willful use of information technology to harm, threaten, slander, defame, or harass another person. With increasing volumes of cyber-aggressive messages, tweets, and retweets, there is a rising demand for automated filters to identify and remove these unwanted messages. However, most existing methods only consider NLP-based feature extractors, TF-IDF, Word2Vec, with a lack of consideration for emotional features, which makes these less effective for cyber aggression detection. In this work, we extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements. The proposed DNN model was tested on the Cyber-Troll dataset. The combination of word embedding and eight different emotional features were fed into the DNN for significant improvement in recognition while keeping the DNN design simple and computationally less demanding. When compared with the state-of-the-art models, our proposed model achieves an F1 score of 97%, surpassing the competitors by a significant spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences for society. This facilitates the spread of rumors on social media. On social media, users share and exchange the latest information with many readers, including a large volume of new information every second. However, updated news sharing on social media is not always this study, we focus on the challenges of numerous breaking-news rumors propagating on social media networks rather than long-lasting rumors. We propose new social-based and content-based features to detect rumors on social media networks. Furthermore, our findings show that our proposed features are more helpful in classifying rumors compared with state-of-the-art baseline features. Moreover, we apply bidirectional LSTM-RNN on text for rumor prediction. This model is simple but effective for rumor detection. The majority of early rumor detection research focuses on long-running rumors and assumes that rumors are always false. In contrast, our experiments on rumor detection are conducted on real-world scenario data set. The results of the experiments demonstrate that our proposed features and different machine learning models perform best when compared to the state-of-the-art baseline features and classifier in terms of precision, recall, and F1 growth of social networking web users, people daily shared their ideas and opinions in the form of texts, images, videos, and speech. Text categorization is still a crucial issue because these huge texts received from the heterogeneous sources and different mindset peoples. The shared opinion is to be incomplete, inconsistent, noisy and also in different languages form. Currently, NLP and deep neural network methods are widely used to solve such issues. In this way, Word2Vec word embedding and Convolutional Neural Network CNN method have to be implemented for effective text classification. In this paper, the proposed model perfectly cleaned the data and generates word vectors from pre-trained Word2Vec model and use CNN layer to extract better features for short sentences find out what other people think has been an essential part of information-gathering behaviors. And in the case of movies, the movie reviews can provide an intricate insight into the movie and can help decide whether it is worth spending time on. However, with the growing amount of data in reviews, it is quite prudent to automate the process, saving on time. Sentiment analysis is an important field of study in machine learning that focuses on extracting information of subject from the textual reviews. The area of analysis of sentiments is related closely to natural language processing and text mining. It can successfully be used to determine the attitude of the reviewer in regard to various topics or the overall polarity of the review. In the case of movie reviews, along with giving a rating in numeric to a movie, they can enlighten us on the favorableness or the opposite of a movie quantitatively; a collection of those then gives us a comprehensive qualitative insight on different facets of the movie. Opinion mining from movie reviews can be challenging due to the fact that human language is rather complex, leading to situations where a positive word has a negative connotation and vice versa. In this study, the task of opinion mining from movie reviews has been achieved with the use of neural networks trained on the “Movie Review Database” issued by Stanford, in conjunction with two big lists of positive and negative words. The trained network managed to achieve a final accuracy of 91%.Duc Duy Tran Sang NguyenTran Hoang Chau DaoSentiment analysis is the interpretation and classification of emotions and opinions from the text. The scale of emotions and opinions can vary from positive to negative and maybe neutral. Customer sentiment analysis helps businesses to point out the public’s thoughts and feelings about their products, brands, or services in online conversations and feedback. Natural language processing and text classification are crucial for sentiment analysis. That means we can predict or classify customers’ opinions given their comments. In this paper, we do sentiment analysis in the two different movie review datasets using various machine learning techniques including decision tree, naïve Bayes, support vector machine, blending, voting, and recurrent neural networks RNN. We propose a few frameworks of sentiment classification using these techniques on the given datasets. Several experiments are conducted to evaluate them and compared with an outstanding natural language processing tool Stanford CoreNLP at present. The experimental results have shown our proposals can achieve higher performance, especially, the voting and RNN-based classification models can result in better with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We not only extract and encode visual and scene text cues but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images with scene text content to demonstrate its effectiveness. In the retrieval framework, we augment the contextual semantic representation with scene text cues to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous scene text recognition, we also apply query-based attention to the text channel. We show that our multi-channel approach, involving contextual semantics and scene text, improves upon the absolute accuracy of the current state-of-the-art methods on Advertisement Images Dataset by in the relevant statement retrieval task and by 5% in the topic classification task.
TextAnalytics is the process of converting unstructured text data into meaningful data for analysis, to measure customer opinions, product reviews, feedback, to provide search facility, sentimental analysis and entity modeling to support fact based decision making. Text analysis uses many linguistic, statistical, and machine learning techniques.
Language Features of a Critical Review Writing a Critical Review Here is a sample extract from a critical review of an article. Only the introduction and conclusion are included. Parts of the Review have been numbered [1] – [12]. Read the extract and match them with the language features listed here a Concessive clauses assist in expressing a mixed response b Conclusion summarises reviewer’s judgement c Introduction d Modality used to express certainty and limit overgeneralising e Offers recommendations f Presents the aim/purpose of the article and Key findings g Qualifies reviewer’s judgement h Reporting verbs i Reviewer ’s judgement j Sentence themes focus on the text k Title and bibliographic details of the text l Transition signals provide structure and coherence [1] A Critical Review of Goodwin et al, 2000, 'Decision making in Singapore and Australia the influence of culture on accountants’ ethical decisions', Accounting Research Journal, no. 2, pp 22-36. [2] Using Hofstede’s 1980, 1983 and 1991 and Hofstede and Bond’s 1988 five cultural dimensions, Goodwin et al 2000 conducted [3] a study on the influence of culture on ethical decision making between two groups of accountants from Australia and Singapore. [4] This research aimed to provide further evidence on the effect of cultural differences since results from previous research have been equivocal. [5] The study reveals that accountants from the two countries responded differently to ethical dilemmas in particular when the responses were measured using two of the five cultural dimensions. The result agreed with the prediction since considerable differences existed between these two dimensions in Australians and Singaporeans Hofstede 1980, 1991. [6] However the results of the other dimensions provided less clear relationships as the two cultural groups differed only slightly on the dimensions. [7] To the extent that this research is exploratory, results of this study provide insights into the importance of recognising cultural differences for firms and companies that operate in international settings. However several limitations must be considered in interpreting the study findings. …. [8] In summary, it has to be admitted that the current study is [9] still far from being conclusive. [10] Further studies must be undertaken, better measures must be developed, and larger samples must be used to improve our understanding concerning the exact relationship between culture and decision making.[11] Despite some deficiencies in methodology,[12] to the extent that this research is exploratory trying to investigate an emerging issue, the study has provided some insights to account for culture in developing ethical standards across national borders. Openswith an appropriate greeting (e.g. Dear Sir/Madam, Dear Mr X / Ms X, Hi, etc) Main body paragraphs. Ends with appropriate closing (e.g. Yours Faithfully, Yours Sincerely, Take Care, etc) 4. HOW TO WRITE A REVIEW. Traditionally, when we think of reviews many of us think of book and movie reviews.

Definition Review text is an evaluation of publication, such as a movie, video game, musical composition, book; a piece of hardware like a car, home appliance, or computer; or an event or performance, such as a live concert, a ply, musical theater show or dance show. Purpose Ø Review text is used to critic the events or art works for the reader or listener, such as movies, shows, book, and others. Ø To critique or evaluate an art work or event for a public audience Generic Structure ü Orientation/Introduction General information of the text. ü Interpretative Recount Summary of an art works including character and plot. ü Evaluation Concluding statement Judgment, opinion, or recommendation. It can consist of more than one. ü Summary The last opinion consist the appraisal or the punch line of the art works being criticized. Language Features Using the present tense Focus on specific participants Using adjectives form example like ad, good, valuable, etc. Using long and complex clauses Using metaphor

Markuplanguage is a system for formatting and arranging the elements in a document using tags. Unlike physical annotations and markups on paper documents, these tags only appear in the document while the author is writing the text. When an application processes the markup, the content will simply appear as formatted text to the viewer. Review Open Access Published 22 February 2022 Future Business Journal volume 8, Article number 3 2022 Cite this article 2656 Accesses 2 Citations Metrics details AbstractThe large number of online product and service review websites has created a substantial information resource for both individuals and businesses. Researching the abundance of text reviews can be a daunting task for both customers and business owners; however, rating scores are a concise form of evaluation. Traditionally, it is assumed that user sentiments, which are expressed in the text reviews, should correlate highly with their score ratings. To better understand this relationship, this study aims to determine whether text reviews are always consistent with the combined numeric evaluations. This paper reviews the relevant literature and discusses the methodologies used to analyse reviews, with suggestions of possible future research directions. From surveying the literature, it is concluded that the quality of the rating scores used for sentiment analysis models is questionable as it might not reflect the sentiment of the associated reviews texts. Therefore, it is suggested considering both types of sources, reviews’ texts and scores in developing Online Consumer Reviews OCRs solution models. In addition, quantifying the relationship degree between the text reviews and the scores might be used as an instrument to understand the quality of rating scores, hence its usefulness as labels for building OCRs solution models. IntroductionWith advancements in and rapid expansion of Web innovations, more and more people are using blogs, forums, Online Consumer Reviews OCRs, and online bulletin boards to comment on their personal experiences. Online Consumer Review OCR platforms present great opportunities to share customer viewpoints, preferences, and experiences on a broad selection of services and products. Therefore, the resulting agglomeration of Online Consumer Reviews OCRs represents a vital information source that consumers can access when selecting a product or service. Gartner research reported in the article “The Future of the Social Customer”, 2012 that 40% of consumers use social media as a search tool, 77% check online reviews, and 75% of consumers feel online reviews are more trustworthy than personal recommendations [30]. Additionally, 81% of online consumers indicated that they received helpful information and advice from these reviews. Therefore, online reviews are not only read but also trusted [6]; this is supported as well by another online consumer survey done by Nielsen Global 2012 [12], in which 70% of respondents trusted the online reviews posted by strangers. In addition to consumer use, OCRs can also be used by commercial enterprises as an openly accessible source of valuable information to better understand preferences and perceptions of consumers. In fact, enterprises can analyse consumer feedback to create effective new strategies for product design [65]. Some companies, such as Sysomos Radian6 or Bazaarvoice support listening and monitoring tools on the web that offer real-time intelligence about the reputations of their customers’ products or services [23].Traditionally, review texts were hard to collect en masse in the “non-connected” world. Online Consumer Reviews OCRs are frequently provided by online review websites in a free-text format, such as Yelp and Amazon. While this comprehensive source of information can help individuals and businesses make better decisions, consumers are faced with the daunting task of locating and reading multiple potentially relevant text reviews, which can lead to an information-overload problem. Consequently, there is a crucial need to mine the available valuable data from reviews to understand user preferences and make accurate predictions and recommendations. To simplify this task and make it more time efficient, certain review websites provide a score averaging the ratings of reviews, in addition to text reviews, as shown in Fig. 1An example of a text review and corresponding ratings [11]Full size imageThe most commonly used scheme for visually displaying average review ratings is the five-star scoring system. Since these scores are only computable using numerical ratings, text reviews should be converted into numerical values or star ratings. There are two ways to do this 1 asking customers to express their opinions on products and services using star ratings or 2 calculate the overall ratings of the text reviews through the use of sentiment prediction techniques. Traditionally, it is assumed that user sentiments, which are expressed in the text reviews, should correlate highly with their score ratings [16, 48]. However, there can be a discrepancy between the text sentiment and the rating, which indicates a non-valuable data source for research studies utilizing review texts or rating scores in their solution models. In addition, some research studies considered only rating scores or review texts, assuming that they were correlated with each other; therefore, these studies might have failed to satisfy their research objectives [35, 47]. This problem has been overlooked in the literature, even though this point helps in identifying the validity of the review to be used for other research studies [3, 66, 67].This paper introduces the concept Text-Rating Review Discrepancy TRRD, which is defined by the inconsistency between text reviews and the rating reviews of a product or service. In addition, this paper provides a background foundation of text reviews and rating, hence demonstrating the need for using text reviews in addition to rating scores for building Online Consumer Reviews OCRs solution models. Therefore, this paper presents a literature review to investigate Text-Rating Review Discrepancy TRRD in the evaluation of text reviews and rating scores in detail. Research papers that utilize machine learning algorithms and natural language processing techniques for opinion mining and sentiment analysis evaluation have been considered. Opinion mining and sentiment analysis are important research topics that identify the underlying sentiments in text reviews. Since identifying the accurate text sentiments of consumers’ reviews of products and services is important for customers, business owners, and product manufacturers, studying the correlation between online text reviews and ratings is crucial to enhance the correctness of sentiment analysis paper examines the following research questions Are there any discrepancies between text reviews and star ratings? If not, are star ratings considered to be good representatives for text reviews? Will the prediction and recommendation accuracy results improve if the correlation between the text reviews and the associated rating scores are first checked? Are available ratings considered as “gold standard,” a high-level representation of ground truth, hence a measurement system whose outputs are known to be accurate and trustable? Ground truth can have wrong labels, it is a measurement, and there can have errors due to human or used machines errors. It should be noted that any trained machine learning model will be limited by the quality of the ground truth used to train and test it. The main contributions of the current study are as follows Discussing the Online Consumer Reviews OCRs existing literature and their applications, Evaluating numeric rating labels used for building Online Consumer Reviews OCRs solution models and showing the necessity of using both text reviews and numeric ratings for building Online Consumer Reviews OCRs solution models. The next following section provides an overview of the importance of studying Text-Rating Review Discrepancy TRRD and why we need to use both text reviews and numeric ratings for building Online Consumer Reviews OCRs solution benefit from studying text-rating review discrepancy TRRDText reviews are a very important source of information for potential consumers before deciding to purchase a product. Consequently, sentiment analysis has a significant impact on products and companies. Many studies used text review to analyse feature specification and customer preferences, assuming the text reviews are consistent with the ratings, which are only general indications of the deduce information for product creation and product feature selection for business benefits, Xiang et al. [64] used consumer reviews to ascertain what customers want from varying types of hotel. They used text analytics to achieve this task and understand customer preferences. As an example, companies can examine comments left online to understand consumers’ feelings or perceptions of a movie and, consequently, predict consumers’ interests [52], or use consumer reviews on products to the same end [32, 50]. Also, a literary contribution by Xiao et al. [65] adds to the literature with a preference-measurement model created from consumers’ reviews. Textual analysis and the use of the results from this mathematical model aid in understanding consumers’ preferences by crowdsourcing from lists of consumers’ online reviews. Subsequently the feedback could then be utilized for such things as product their research, Li et al. [30] proposed a social intelligence mechanism that could extract and consolidate reviews given using social media and gain critical insights into new product or service features to assist the decision-making process for the development of new products or services by analysing the reviewers' opinions, authority, and understanding knowledge as well as sentiment towards targeted products. Khalid et al. [27] highlighted some of the issues raised by consumers for mobile-app reviews additional cost, practicality, compatibility problems, crashing. They highlighted a statistical depiction of some of the consumer reviews from the Apple App Store and Google Play. In 2014, Vu et al. [62] proposed a keyword-based framework in gathering and getting consumer reviews from the app stores by taking out, evaluating, and categorizing keywords based on semantic resemblance. Additionally, they created an image tool that showed the occurrence of these keywords over a certain period of time and accounted for any suspicious patterns. Park et al. [42] fashioned an app, AppLDA, to be used on app narratives and consumer reviews as a subject model. Using this method, an app developer can examine reviews as well as establish what are seen as essential app features. An automatic system of categorizing customer reviews in regard to programmed classification was offered by Panichella et al. [41]. The system was designed to support the software upholding and requirement and Kim [21] recommended the use of SUR-Miner to summarize and categorize reviews. They evaluated SUR-Miner on 17 Google Play apps such as Swiftkey, Camera360, WeChat, and Templerun2. They randomly selected and assessed 2000 sentences from text reviews. From different points of view, Mcilroy et al. [36] analysed this study’s glitches by looking at the developers of the top apps such as Apple and Google. They observed that there were optimal results, which came from developers replying to the reviews—consumers. This has a positive effect on the reviews as the average ratings has increased by and the median rating by 20%.All discussed research papers above are offering Online Consumer Reviews OCRs solution models which use either text review, associated rating numeric or both to build their models. We advise to investigate Text-Rating Review Discrepancy TRRD to ensure the validity and correctness of the built solution model. Thus, this paper discusses this topic in detail and is laid out as follows “Background” section presents background knowledge in the domain of text reviews and ratings, then “Methods” section presents the survey methodology. “Related work” section reviews work on Online Consumer Reviews OCRs models and applications and introduces the relevant studies that consider the discrepancies between text reviews and ratings. This study first identifies research that focuses only on one source of information—either text reviews or rating scores—then identifies research where both approaches were examined. “Results and discussion” section presents a guideline proposal based on the results of the survey. “Conclusion” section concludes the paper and indicates future and related research section provides an overview of the most relevant topics related to text sentiment analysis. First the definition of Online Consumer Reviews OCRs is provided; then, sentiment analysis-related topics were identified and consumer reviews OCRsOCR is one of the most commonly used concepts to represent the traditional word-of-mouth review. An electronic word-of-mouth review, or OCR, is defined as “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” [24]. Word-of-mouth review, meaning personal opinions among people, has been recognized as a significant source of information to understand customers’ interests, and sentiments concerning companies’ products and services, such as movies, books, music albums, and enterprises such as hotels, and restaurants. Many consumers find word-of-mouth information to be useful and credible when making a decision about products or services because it is generated by independent pre-experienced consumers instead of biased company advertisements. With the rapid advancement of Internet technology, the electronic word-of-mouth technique has been adopted by different platforms such as Yelp, Amazon, and eBay to enable people to easily generate reviews, share them with other people, and exchange opinions. Electronic word-of-mouth information includes customer reviews, online comments, and score ratings, and it can be spread in real-time through online channels, such as e-commerce sites, online forums, the blogosphere, and social networking sites. Thus, electronic word-of-mouth information is recognized as not only a convenient way for consumers to share information, but also a source of new challenges and opportunities for business analysts to understand consumer interests and also support reliance on Online Consumer Review OCR for the decision-making of consumers. Nearly 65% of consumers access consumer-written product reviews via the Internet [15]. Additionally, of those consumers who read reviews, 82% confirmed that reviews had directly influenced their buying decision, while 69% shared the reviews with others including family, friends, and co-workers, so magnifying their impact. In addition, numerous surveys and consulting reports have suggested that, for a number of consumers and products not applicable to all, consumer-generated reviews are valued more highly than reviews from ‘experts’ [21, 61]. Therefore, Online Consumer Reviews OCRs can impact the consumer decision-making process to a greater extent than traditional media [1].Text mining and sentiment analysisData and text mining cover the broad scope of software tools and mathematical modelling approaches which are used to discover implicit, previously unknown patterns from data. In text mining, patterns are lifted from natural language text unstructured data, while, in data mining, the patterns are lifted from structured databases. The text mining process starts with the text collection stage and then proceeds to the pre-processing stage in which the text is cleaned and formatted. The pre-processing stage involves critical tasks, such as tokenization, removal of stop words, and stemming. In the next stage, meaningful features are extracted to make inferences about the data. In the final stage, text mining approaches, such as categorization, topic modelling, or clustering, are applied to answer certain questions about the given volumes of Online Consumer Reviews OCRs are available on many retailers' websites, and mining such data to understand consumers’ opinions is called opinion mining. This term was first coined by Dave et al. [14], where opinion mining involves processing a set of reviews for a given product or service and extracting a list of attributes or aspects to categorize the consumer opinion into different classes such as positive, negative, subjective, objective, etc.. Sentiment analysis is a subsection of opinion mining which focuses on the extraction of the consumer's emotions, opinions, and evaluations on services or products from online reviews they have posted. Sentiment analysis is an area of research that is very active, with a large volume of relevant research literature available [3]. Most of the research work is typically based on three common sentiment analysis tasks subjectivity analysis, polarity detection, and sentiment strength detection. Subjectivity analysis aims to determine whether or not a given text is subjective, while polarity detection is utilized to assign an overall positive or negative sentiment orientation to subjective texts. Sentiment strength detection specifies the degree of polarity to which a text is either positive or analysis is normally performed in two ways a lexicon-based approach or a machine learning approach. A lexicon-based method uses a sentiment dictionary or a sentiment lexicon that is used to predict the overall sentiment of a text based on pre-defined word occurrence. Alternatively, a machine learning approach generates a classifying algorithm through learning with the set of linguistic features [28]. The trained classifier is then used for sentiment prediction [3].In the lexicon-based approach, public lexicons, such as SenticNet [10], SentiWordNet [2], and OpinionFinder [63], have been frequently applied by many studies owing to the reliability of public sentiment dictionaries [28]. Lists of sentiment-bearing words and phrases available in opinion lexicon are used for lexicon-based techniques, such as the General Inquirer lexicon [54], WordNet Affect [55] SentiWordNet, the ANEW words [8], and the LIWC dictionary [43]. Beyond these standard resources and to automatically generate and score lexicons, researchers have created new methods. However, as indicated by Liu Y [34], while an opinion lexicon is required, it is insufficient for sentiment analysis. Thus, a combined approach is more appropriate as these approaches normally use additional information, semantic rules to handle emoticon lists, negation, booster word lists, and an already existing and substantial collection of subjective logical statement patterns. According to Taboada et al. [56], “lexicon-based methods for sentiment analysis are robust, result in good cross-domain performance, and can be easily enhanced with multiple sources of of Experience QoE parameters were utilized by [61] to analyse user reviews. The identification of frequent nouns in reviews was achieved through the utilization of speech tagging, and these were denoted as a prospective QoE element. Semantic lexicons, such as SentiWordNet, were used to group and aggregate similar nouns. For each group, the representative nouns were highlighted as QoE parameters. This work, therefore, exploited user reviews as inputs for quality element extraction from services through the selection of frequent nouns in drawing features and the end machine learning algorithms have been used for most existing sentiment analysis techniques, such as Naive bayes NB, support vector machines SVM, neural network NN, genetic algorithm GA, and k-nearest neighbours kNNs to optimize, classify, and form predictions based on the data in text documents. Machine learning approaches have certain advantages, including the ability to identify the non-sentiment terms which express a sentimental judgement “cheap” in the phrase “this camera is cheap”. An additional advantage of such approaches is the availability of a wide range of applicable learning algorithms. However, these methods present certain disadvantages, such as the need for a human-labelled corpus for the training phase. Additionally, while within the domain these trained machine learning methods perform very well, their performance can diminish significantly when applied to another domain. For example, in the cell phone domain, the words “cheap” and “smart” are used as expressions of positive opinions, while in the world of books domain, “well-researched” and “thriller” signify positive sentiments. Therefore, a cell phone domain-trained algorithm is unlikely to correctly classify book domain reviews. Moreover, as indicated by [58], some machine learning algorithms cannot “give a clear explanation as to why a sentence has been classified in a certain way, by reference to the predefined list of sentiment can principally investigate sentiment analysis applications at three granularity levels document level, sentence level, and aspect level. At the document level, the entire document is allocated an overall sentiment score. Sentence-level sentiment analysis concentrates on predicting the sentiment of stand-alone sentences. Subsequently, a score aggregation method is applied to generate an overall review score from combined sentence-level scores. However, in a document- or sentence-level analysis, it is not easy to obtain fine-grained opinions, though an aspect-level analysis can frequently overcome this problem. Aspect-level techniques carry out a finer-grained analysis with the intention of identifying sentiments on entities and/or their aspects [65].Challenges in using sentiment analysis with OCRsThere are certain challenges and problems in implementing sentiment analysis, and some of them are as followsShort reviews in a proposal by Cosma et al. [13] they state that in order to surmount the domain barrier in gathering views, there is need for an overall way of setting up language rules for the recognition of view-bearing words. Additionally, online reviews have unique text features that are short in length, use formless phrases, and involve substantial data. New challenges to conventional study topics in text analytics, text categorization, data mining, and emotional studies, are brought to the short language is another vital attribute of online text, specifically in online reviews. Consumers may use short forms or acronyms that rarely appear in traditional text when writing reviews, for example, phrases like “superb”, “good 2go”, hence making it extremely hard for one to identify the semantic meaning [5].Mockery acknowledgement the varied challenges require working through mockery or expressions that are unexpected. Riloff et al. [44] contributed to the improved review in mockery acknowledgement by developing an algorithm that naturally learned to group good and unpleasant phrases for tweets. The evaluation of two elements that are dissimilar amounts to dependency the essential task of exploring the information generated by the customer lies in the wider concept of themes. Generally, the content generated is usually broad and needs to be packaged into categories. A classifier that is specified for a given domain space might thus not be effective in another domain which uses different words. The expression of sentiments is varied in different domains. This, notwithstanding the methods of sentiment categorization, can be synchronized to adequately work in a provided domain but, at the same time, may be limited to categorize sentiments in a varying domain. In light of this Bollegala et al. 2013 [7] proposed a cross-domain sentiment classifier that automatically extracts a sentiment thesaurus. Moreover, procedures or algorithms that are joined in a given area may not necessarily perform effectively in a space that is different from the initial one. The process of identifying specific area- and space-free systems was independently carried out. Jambhulkar and Nirkhi [25] carried out a cross-domain sentiment analysis survey study that focused on the following methods sentiment-sensitive thesaurus, spectral feature alignment, and structural correspondence learning. The findings of the study denoted that each of the used methods has its distinct way in 1 increasing the vector features, 2 evaluating the association between given words, as well as 3 the used classifier. According to Bisio et al. [4] there are two main features of notion characterization. These include the versatile nature of a provided structure and the subsequent ability to work in wider business spaces through utilization of relevant valence shifters, semantic systems, and a predictive model grounded on are mainly three broad types to conduct a literature review including the systematic review, the semi-systematic review, and the integrative review [53]. For this research work, an integrative review of the literature was undertaken to critically analyse and examine the outcomes reported in related studies investigating different OCR solution models. An integrative review approach can be useful when the purpose of the review is not to cover all articles ever published on the topic but rather to combine different perspectives to effectively identify current problems and generate new knowledge about the topic [59]. In addition, identifying and analysing developed solution models using OCR is a broad topic, and a variety of disciplines such as business, marketing, and computing address various aspects of it. Therefore, we believe that using an integrative review would be a good choice for studying a broader topic that has been conceptualized differently and studied within diverse such as Web of Science, Google Scholar, Scopus, and ScienceDirect have been accessed to search for existing research literature and documents relating to the topic. In addition, relevant research papers were accessed through backward citations of the articles included in the review. Relevant search terms text reviews, star ratings, score ratings, and text-ratings correlation have been used to identify studies from 2002 to 2020. Then, to enhance the literature, we incorporated more keywords online reviews, product reviews, online recommendations, online word-of-mouth e-WOM, online viral marketing, online consumer reviews, online communities, and virtual communities to obtain articles from numerous management journals and relevant databases, including the Association for Computing Machinery Digital Library ACM, IEEE Xplore, SCOPUS Elsevier, and ScienceDirect Elsevier. Papers’ selection was structured in a two-stage process first, excluding research studies based on reading the titles and the abstracts. In the second stage, research papers were filtered again from the initially selected list of papers, based on a complete reading. Around 66 papers with a minimum number of citations per paper 3 which were relevant, addressing the research questions, and contributing to the basic purpose of the review have been included in the ontology to conceptualize knowledge in the domain of text reviews has been proposed. Protégé [38] was used to build the ontology that includes the main concepts in the domain of review and review analysis. The ontology also determines the relationships between the concepts. Figure 2 shows the proposed review ontology which consists of 31 classes to conceptualize and classify the concepts of review domain based on the analysis of this 2Review ontologyFull size imageRelated workReviewed papers are broadly first categorized into two themes 1 research studies that built their models based on either rating scores or review texts, assuming that they were correlated implicitly with each other. In this part, we included research studies that have not considered any validation metrics to compute the relationship degree between available rating scores and other labelling techniques. They only use one source of labelling, only numeric ratings, or review texts’ sentiments, to build their OCR solution models. 2 Research work that examined the correlation between the text reviews’ sentiments and the associated rating scores before applying the proposed solution model. Hence, numeric rating scores have been validated using other labelling techniques such as review texts annotation using either experts or sentiments secondly, for each categorized theme, all relevant literature was classified into categories based on the following criteria research studies use OCR on survey analysis, research studies use OCR for prediction/ classification models, and research studies use OCR for recommendation models. Summary tables for the reviewed research studies are presented at the end of each subsection. We examined the reviewed papers based on the following considerations used model, domain, manually labelled reviews were labelled by experts, and automatic labelling reviews were labelled by sentiment lexicons. For research studies that examined the correlation between the text reviews’ sentiments and the rating scores, we checked, as well, the degree of consistency between text reviews and rating scores. Figure 3 shows the adopted framework for conducting the review. All relevant literature used in this paper are provided in Appendix. Fig. 3Framework adopted for conducting the reviewFull size image 1. Numeric rating labels have not been validated against other labelling techniques In surveying the literature, we found that many research studies have not considered any validation metrics to compute the relationship degree between available rating scores and other labelling techniques, whether they were survey papers or sentiment classification using machine learning algorithms or other different techniques. In the context of online reviews survey analysis, Shoham et al. [51] examined the effect of irrelevant reviews and its associated positive or negative rating scores on customers' product evaluations and future decisions. The survey analysis study was done using 7913 reviews of approximately 100 products in five different classes. The survey results revealed that the presence of irrelevant reviews with negative rating scores alongside positive reviews leads to greater product preferences, as consumers feel confident that the information they have about the product is more complete. This finding suggests that sellers or service providers should not be discouraged by negative or totally unhelpful, irrelevant reviews, or attempt to block customers from seeing research studies that apply machine learning ML algorithms for sentiment classification, Kim, Kwon, and Jeong [28] detected the availability of machine learning models only using linguistic features and identified the influence of the size of the linguistic feature set on the classification accuracy. They conducted sentiment analysis study focusing on Korean electronic word-of-mouth eWOM in the film market and selected 10,000 movie reviews, which were rated with negative and positive popularity on the movie portal sites, and parsed words through natural language processing NLP. Four machine learning methods Naïve bayes NB, decision tree DT, neural network NN, and support vector machine SVM were demonstrated with the linguistic features, and their performances were compared by accuracy and the harmonic mean between precision and recall F1 score. In addition, Kim et al. tested five different feature set sizes groups to see whether the feature set size influenced the performance of classification. As a result, neural network NN and support vector machine SVM classification showed acceptable performance under every condition. Through the experiments, Kim et al. showed how machine learning algorithms are applied as sentiment classifiers for movie electronic word-of-mouth eWOM analytics, and a performance gap might have occurred with this method as a result of the feature set size. Another study done by Rui, Liu, and Whinston [46] employed support vector machine SVM and Naïve bayes NB classifiers to assess word-of-mouth eWOM impact of consumer’s willingness. They categorized 4,166,623 movie tweets into four mutually exclusive categories intention, positive, negative, and neutral. Researchers trained NB for intention and support vector machine SVM for sentiment, and validated word-of-mouth eWOM impact of tweets with precision and recall as performance et al. [22] proposed also a machine learning senti-word lexicon based on training support vector machine SVM algorithm using Amazon corpus containing reviews from various domains. They converted Amazon reviews dataset into binary classes positive and negative by assigning reviews with a 1 or 2 rating scores as negative reviews and the reviews with a 4 or 5 rating scores as positive reviews, while ignoring reviews with a 3-star rating scores. They have provided an upgrade for creating a lexicon by using ‘Strong Reviews’ for the dataset and ‘root’ of tokens as the linguistic feature used by support vector machine SVM. An additional improvement in the accuracy of reviews classification comes from using ‘Term Score Summation’ for sentiment computation. Pang et al. [40] applied three machine learning methods Naïve bayes NB, support vector machine SVM, and maximum entropy MaxEnt to determine whether a movie review was positive or negative. A corpus of 752 negative and 1301 positive reviews, with a total of 144 reviewers from Internet Movie Database IMDb archive, has been used for the study. They examined several features conditions, such as n-gram, parts of speech POS, and position of the word. They achieved a best performance using 16,165 unigrams features and support vector machine SVM method by accounting only for feature presence. Interestingly, their experiment also showed that using top frequent 2633 unigrams, accuracy was very similar to the best performance noted above. This means that a small-sized feature set can be considered as an efficient method for sentiment analysis of big data [28].Prediction of review numeric rating scores is one of the main tasks of sentiment analysis, and it stretches the binary sentiment classification and focuses more on predicting the numeric rating 10 stars for a given review. Pang and Lee 2005 [39] looked into prediction of a review rating as a classification regression challenge; therefore, they created a rating predictor with machine learning method under a supervised metric labelling framework. They proposed a meta-algorithm using metric labelling to ensure that similar items receive similar labels. The results showed that the proposed model outperformed both multi-class and regression versions of support vector machine SVM.Through taking into account user information, Tang et al. [57] proposed a neural network method for review rating prediction. In their paper, they targeted a finer-grained document-level problem and conducted experiments on two benchmark datasets, Yelp13 for restaurant reviews and RT05 for movie reviews. They used two main models the user-word vector model which modifies the original word vectors with user information, and the document vector model which takes the modified word vectors as input and produces review representation that are used as the feature for predicting review rating. The proposed method marginally outperformed text-based neural network algorithms convolutional neural network CNN, for the following datasets Yelp and RT, as they captured user-level and text-level semantics et al. [3] detected the polarity of reviews by adopting a machine learning technique, and then, they considered sentence scores as proof for overall review ratings. In order to predict review scores, they first discover the individual sentences’ scores within a review and then group them into five-star review scales. To detect emotions at the sentence level, they used SentiStrength, an available library for lexicon-based sentiment strength detection. Experiments were carried out on CitySearch for restaurants reviews and TripAdvisor for hotels reviews. The results showed that the proposed model outperforms the existing aggregation methods with regard to accuracy and mean absolute error MAE. However, the proposed model does not perform well compared to some machine learning algorithms such as AdaBoost, Bayesian Networks, Decision Tree DT, K-Star, Naive bayes NB, and support vector machine SVM in terms of accuracy. The main advantage of the proposed model is that it outperforms other machine learning algorithms in terms of speed and memory requirements. Table 1 summarizes the reviewed papers that did not consider any validation procedure to examine the relationship between texts reviews and numeric ratings. Table 1 Summary of the reviewed papers in which no validation procedure has been applied to examine the relationship between numeric ratings and texts reviewsFull size table 2. Numeric rating labels have been validated against other labelling techniques In this subsection, a sample of research studies that have examined the relation between numeric ratings and text reviews is discussed and reviewed. Starting with Zhu et al. [68] who examined the link between guests’ text reviews and score ratings in the tourism and hospitality domain, based on the text reviews of 4602 Airbnb accommodation listed in San Francisco, USA, the main finding was there is a strong relationship between the positive negative sentiment and the high low score ratings. People tend to give higher score rating for positive reviews and low score rating for negative reviews. They applied the Tobit model and results indicate that there is a higher rating score can be expected if the guest’s comment was more positive and the opposite for the negative [29] examined review reliability by using sentiment analysis which was based on reviews left by travellers with Skytrax and connected Twitter messages. This study examined the extent to which sentiments within reviews about air experiences with Skytrax correlated to the Star-Airline Ratings 1–5, and how travellers’ feelings on air travel experiences differed on Skytrax to those left on Twitter. Results showed that the Airlines Rating programme 1–5 stars actually had a low level of reliability based on what airlines knew what had been posted on Twitter. Two tests revealed that there is a nominal positive correlation between sentiments within reviews from Skytrax compared to Star-Airlines ratings In addition, the Airlines Rating programme clearly reveals a fragile external validity. Although text sentiments from Skytrax and Twitter had a positive correlation, the level had statistical significance. In total, 4033 Skytrax reviews were used for the analysis in addition to 10,522 tweets, and related comments for 177 airlines were gathered by individually searching under each airline’s unique et al. [18] investigated the relationship between customer online review sentiments and guests’ hotels ratings. They examined if the customer sentiment polarity had a positive effect on their ratings. Results that have shown consistency between customer reviews and hotel ratings are not entirely consistent across budget and premium hotel categories. It explains the sense of 44% of the variance in the customer rating for budget category and 21% of the variance in customer rating for premium category. They found that there is a linear relationship between customer rating and customer sentiment and Prendergast [60] investigates how the inconsistency of positive or negative reviews between text reviews and ratings affects the consumers and shows that there is a link between text reviews and ratings. It was found that text positively or negatively significantly influenced consumers’ reactions of reviews. Sellers can benefit by incorporating both text reviews and ratings to enhance the prediction accuracy of the products' sales. In the survey results, they collected 30 responses for each of the 24 releases which formed a sample size of 720. They collected the data in three high-traffic areas in Hong Kong and assigned the reviews to participants randomly. After reading them, their understanding was measured. Then, they conducted the manipulation checks and collected their demographic et al. [16] compared the users’ star rating with text reviews using Pearson correlation coefficient which ranges from − 1 to 1. Using a corpus contains 5531 restaurants, with associated a set of 52,264 reviews. Reviews contain structured metadata star rating, date along with text. The experiment showed there was a positive correlation between positive reviews and star ratings and a negative correlation between negative reviews and star ratings. These results motivated the authors to include text reviews in the context of recommender systems. Research hypothesis is that the review text is a better indicator of the review than the coarse star rating. They test this hypothesis in the recommendation system scenario and explore whether text-derived ratings are better predictors than numerical star ratings given a user’s restaurant addition to the application on prediction models, considering both score ratings and text review plays a vital role in recommendation systems. In order to recommend products to users we must finally predict how the user responds to a new product. To do this, we must disclose the implicit tastes of each user as well as the characteristics of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to determine that the book is about wizards, as well as the level of the user's interest in wizardry. User feedback is required to discover these inherent dimensions of the product and the user. This feedback often comes in the form of a numeric rating accompanied by the review text. However, traditional methods often ignore review text, making it difficult to fully interpret user and product dimensions, as they discard the same text that justifies a user's et al. [67] proposed a transformation that links the users’ or items’ average rating with sentiment probability to better rating prediction. They transform the average rating of items to the sentiment distribution in the text reviews and map the average rating score into a probability space of the sentiment distribution. Using a real dataset from Amazon, they found that mean squared error MSE using their model achieved the smallest one, and thus performed the best among all considered models. Ling et al. [31] proposed a generative model that combines a topic model with a rating model. Experiments show that the proposed model leads to significant improvement compared with strong baseline methods, especially for sparse datasets where rating-only methods cannot make accurate predictions cold-star setting.The researchers McAuley and Leskovec [35] indicated that most of the research work in the domain of reviews and rating were studied disjointedly. Therefore, the authors proposed a methodology for predicting reviews accurately and for genre automated discovery by combining both text reviews and rating. In addition, the authors pointed out that the research area for studying reviews includes understanding the rating process and predicting rating. In their paper, the authors found out that predicting review accuracy can be increased by combining text and 2 and 3 summarize the reviewed papers which use a validation tool to examine the relationship between texts reviews and numeric ratings for both prediction and recommendation models, 2 Summary of the reviewed papers which examine the relationship between numeric ratings and texts reviews in prediction modelsFull size tableTable 3 Summary of the reviewed papers which incorporate both numeric ratings and texts reviews in recommendation modelsFull size tableResults and discussionAfter surveying the literature, in most of the reviewed papers, sentiment labels are obtained from the review text or the associated rating scores. We argue that there are differences between the sentiments of the reviewed text and the associated numeric ratings and ought to be considered. This issue has been largely ignored and only some studies such as [18, 67, 68] have partially taken it in their considerations while building their solutions. In addition, reviewed research papers that assessed the relationship between text reviews and the associated rating scores have revealed low to average correlations. This analysis result suggests that building solution models based on only the texts’ sentiments or the numeric rating scores should be used with caution in should be noted that this research paper identifies the problem of inconsistency between text review and numeric scoring and how this might question its usefulness as labels for building OCRs solution models. Hence, a non-tested correlation or even weak correlation suggests or implies for inaccurate labels which affect the model outputs. Otherwise stated, when developed models learn from inaccurate labels, they output inaccurate predictions and recommendations. Other research work has discussed discrepancies sources and indicated that review texts do not correlate well with the review outcomes may be results of random errors or the subjective process involved in presenting the review. The source of discrepancies has been examined by Geierhos et al. [19] and Jang and Park [26]. In this regard, Geierhos et al. [19] pointed out that one of the reasons for the inconsistency is individual random errors, while Jang and Park [26] attributes this position to two possible sources of uncertainty reference uncertainty reviewers are affected by previous reviews and reference heterogeneity reviewers have different backgrounds and experiences. Mellinas et al. [37] and Sharma et al. [49] in their analysis concluded that customers tend to punish dissatisfaction more harshly than in this work, we propose some of the guidelines that can help reduce the effect of discrepancy between text reviews and ratings. The following subsections introduce guidelines we derived using the results of our guideline to incorporate text reviews and numeric ratingsTo overcome the available Text-Rating Review Discrepancy TRRD shortcoming, we suggest measuring  text reviews and ratings correlation and consider their agreement and disagreement level into account. For example, to build a model, which predicts the review rate for a given text, we could select the training data instances text reviews that have an agreement between the experts’ annotations, numeric ratings, and sentiment lexicon results. To build prediction models, we propose the following steps shown in 4. It starts with annotating data from different sources such as experts, numeric ratings, and sentiment lexicons. Then, a measure of agreement should be used to reflect the amount of agreement for the annotated reviews using different labelling sources. Finally, the review instances that have a strong correlation between its annotation values using different methods would be selected for building the model that predicts the review rate for a given 4The enhanced model for prediction modelsFull size imageIn the case of the recommender models, we also propose to incorporate both text reviews and numeric rates in order to understand users’ preferences and interests and therefore deliver better customer service. Figure 5 illustrates the proposed model for building recommendation models. It should be noted that the illustrated steps in 4 and 5 are the basic steps of the development of any prediction/recommendation model. The only added phase was to ensure the agreement between text reviews and associated numeric 5The enhanced model for recommendation modelsFull size imageValidation measuresIn this study, we propose to examine the correlation between available rating scores, rating scores provided by annotators, and scores calculated using lexicon-based models. The proposed parallel set of measures to compute the used labels’ validity are listed in Table 4. The provided evaluation framework encompasses a set of quantitative measures that provides estimated results of the instances’ labels’ 4 Quantitative measures to compute the relationship degree between available rating scores and other labellingFull size tableConclusionThe use of Online Consumer Reviews OCRs has attracted researchers across multiple disciplines such as business, marketing, and computing. In most proposed solutions for analysing the online customers reviews, rating scores, and review texts were the primary components that have been employed to produce high-quality Online Consumer Reviews OCRs solution models. However, most of the reviewed models consider either rating scores or the review texts to build their models assuming that both were correlated with each other. This paper introduces the concept Text-Rating Review Discrepancy TRRD which is defined by the inconsistency between text reviews and score ratings for a product or service posted review. The main contribution of this paper includes showing the necessity for using both text reviews and score ratings to ensure having reliable survey results and building valid models. We therefore reviewed the literature to identify if there are any discrepancies between text reviews and numeric ratings. In surveying the literature, we found that research studies that assessed the relation between text reviews and the associated rating scores have revealed low to average correlations. This finding suggests that building solution models based on only the texts’ sentiments or the numeric rating scores should be used with caution in practice. Alternatively stated, the presented exploratory analysis shows that customers might express text sentiments which are different from the associated numeric rating scores. Therefore, we propose to take full advantage of the abundant information of the text reviews sentiments and examine its relationship degree to the combined rating scores. Then, employ the most correlated data instances in order to build a more accurate model. Our research suggests that sentiment of a review combined with a correct numeric rating would be an indicator for the validity and correctness of the required OCR solution model. Also, this study encourages researchers to look beyond the numeric ratings into the text sentiments as written texts can express more information and emotions which quantitative ratings cannot capture. To end with, future research should attempt to ensure the correctness and quality of both the text review and associated numeric ratings. In addition, it should also pay more attention to the causes of discrepancies and inconsistencies between text reviews and ratings in order to mitigate and reduce its negative effects on developed OCRs solution models. 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The authors, therefore, gratefully acknowledge DSR technical and financial informationAuthors and AffiliationsFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaAmal Almansour, Reem Alotaibi & Hajar AlharbiAuthorsAmal AlmansourYou can also search for this author in PubMed Google ScholarReem AlotaibiYou can also search for this author in PubMed Google ScholarHajar AlharbiYou can also search for this author in PubMed Google ScholarContributionsAM, RO, and HH contributed to the design and execution of the research review, to the analysis of the results, and to the writing of the manuscript. All authors have read and approved the authorCorrespondence to Amal declarations Competing interests The authors declare that they have no Competing interests. Additional informationPublisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional following table summarizes both 1 research studies that built their models based on either rating scores or review texts, assuming that they were correlated implicitly with each other. 2 research studies that examined the correlation between the text reviews’ sentiments and the associated rating scores before applying the proposed solution Rating Labels Have Not Been Validated Against Other Labelling TechniquesPaper AuthorsPaper TitleBasiri et al. [3]Sentiment prediction based on Dempster-Shafer theory of evidenceHamouda et al. [22]Building machine learning based senti-word lexicon for sentiment análisisKim et al. [28][Comparing machine learning classifiers for movie WOM opinion miningPang and Lee [39]Seeing stars Exploiting class relationships for sentiment categorization with respect to rating scalesPang et al. [40]Thumbs up? Sentiment classification using machine learning techniquesRui et al. [46]Whose and what chatter matters? The effect of tweets on movie salesShoham et al. [51]Positively useless Irrelevant negative information enhances positive impressionsTang et al. [57]User modeling with neural network for review rating predictionNumeric Rating Labels Have Been Validated Against Other Labelling TechniquesPaper AuthorsPaper TitleGanu et al. [16]Beyond the stars Improving rating predictions using review text contentGeetha et al. [18]Relationship between customer sentiment and online customer ratings for hotels An empirical analysisLi [29]Application of sentiment analysis Assessing the reliability and validity of the global airlines rating programLing et al. [31]Ratings meet reviews, a combined approach to recommendMcAuley and Leskovec [35]Hidden factors and hidden topics Understanding rating dimensions with review textTsang and Prendergas [60]Is a “star” worth a thousand words? The interplay between product-review texts and rating valencesYu et al. [67]Rating prediction using review texts with underlying sentimentsZhu et al. [68]Sentiment and guest satisfaction with peer-to-peer accommodation When are online ratings more trustworthy?Rights and permissions Open Access This article is licensed under a Creative Commons Attribution International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit Reprints and PermissionsAbout this articleCite this articleAlmansour, A., Alotaibi, R. & Alharbi, H. Text-rating review discrepancy TRRD an integrative review and implications for research. Futur Bus J 8, 3 2022. citationReceived 27 September 2021Accepted 25 January 2022Published 22 February 2022DOI analysisText reviewRating scoreCorrelation
Emotivelanguage is the best form of language to connect with the audiences, be it through written medium or verbal. For Example: Non-Emotive: The government has reduced the gasoline priced. Emotive: The government has slashed the price of gasoline. Explanation: Notice that just by changing the word "reduced" to "slashed", the emotional
Kamu termasuk orang yang senang dengan karya orang lain? Jika iya, bagaimana kalau sekalian menilai karya-karya tersebut melalui review text? Yuk, kenali review text terlebih dahulu melalui artikel ini! — Yes, hai everyone! Pembahasan mengenai jenis teks dalam bahasa Inggris masih berlanjut, nih. Terakhir, English Academy sudah berhasil mengajakmu untuk mempelajari tentang recount text, spoof text, dan news item text. Sekarang, artikel ini akan menyajikan penjelasan mengenai review text. Jenis teks satu ini cocok bagi kamu yang detail oriented. Pasalnya, review text adalah sebuah tulisan yang berkaitan dengan bagaimana seseorang menilai sesuatu secara rinci. Oke, kita akan langsung ke pembahasan ini ya, mulai dari definisi review text, struktur, tujuan, dan juga contohnya. Keep going on! Apa Itu Review Text Pengertian dari review text adalah jenis teks dalam bahasa Inggris yang berisi ulasan, evaluasi, tinjauan, atau penilaian dari sebuah produk. Dalam hal ini, produk bisa mengacu pada banyak hal, mulai dari publikasi berbentuk buku, film, musik, video, etc. Selain itu, jika kamu juga bisa menuliskan ulasan dalam bentuk review text untuk barang atau jasa, lo. Misal kamu menuliskan ulasan tentang merek mobil, skincare, wedding organizer, dan masih banyak lagi. Kalau mengutip dari British Course, review text jika didefinisikan dalam bahasa Inggris adalah, “Review text is an evaluation of a publication, such as a movie, video game, musical composition, book; a piece of hardware like a car, home appliance, or computer; or an event or performance, such as a live music concert, a play, musical theatre show or dance show.” Nah, dalam review text, biasanya penulis akan menjelaskan bagaimana kekurangan dan kelebihan dari produk yang mereka gunakan. Jadi, memang idealnya penulis harus merasakan atau menggunakan terlebih dahulu produk/karya yang dipakai untuk bisa membuat teks review. Apa Saja Struktur Review Text Generic Structure of Review Text Seperti biasa, ketika ingin membuat teks apa pun itu, pasti ada struktur yang bisa jadi acuan bagi penulis. Review text tersusun dari 1. Introduction As always, pada bagian awal akan selalu ada pengenalan. Kalau dalam narrative text, bagian ini disebut juga dengan orientasi. Orientasi dalam teks review disebut dengan introduction. Dalam introduction atau orientasi, kamu wajib memperkenalkan suatu produk/karya yang akan dibahas pada audiens. Mulai dari apa nama produknya, siapa yang membuatnya, sejarahnya, fungsi dan kegunaannya, atau gambaran umum mengenai sesuatu yang akan di-review. Jangan lupa untuk mengenalkan karya atau benda tersebut dengan jelas agar pembaca tidak misunderstanding ya. Terlebih, dalam penulisan nama karya atau produk jangan sampai ada salah penulisan atau typo, guys. Penasaran sejauh mana kemampuan bahasa Inggris kamu? Yuk, cari tahu melalui Placement Test by English Academy! Ssst, tesnya bersertifikat dan kamu akan berkesempatan untuk berkonsultasi langsung dengan Personal Consultant English Academy, lo! Yuk, cobain sekarang! 2. Evaluation Struktur yang selanjutnya adalah evaluation. Dalam bahasa Indonesia, tentu artinya adalah evaluasi. Pada bagian ini, kamu bisa menggambarkan suatu produk atau karya dengan lebih detail. Well, di paragraf evaluation umumnya berisi tentang kelebihan, keunikan, kualitas, dan apa yang menurutmu mencolok dari sebuah karya atau produk. Contohnya, kalau kamu membuat review text tentang skincare B untuk kulit berjerawat, maka sebutkan apa saja kandungan dari skincare tersebut, bagaimana kemasannya, lalu apakah efektif untuk menyembuhkan jerawat, dan lain sebagainya. Ingat, jangan sampai membuat review text jika kamu tidak mencoba dan menikmati suatu produk/karya terlebih dahulu ya! 3. Interpretative Evaluation akan diikuti dengan interpretation. Sebagian orang menyebut bagian ini dengan interpretative recount, yaitu paragraf yang berisi pandangan penulis terhadap karya atau produk yang diulas. Nah, dalam interpretative juga kamu bisa melakukan komparasi alias menunjukkan perbandingan dengan karya atau produk lain yang sejenis. Tujuan dari perbandingan tersebut adalah untuk memperkuat pandangan penulis terhadap suatu produk. Misal, Caca mengulas serum pencerah wajah merek A dan membandingkannya dengan merek B. Kemudian Caca berpendapat bahwa serum merek A lebih worth to buy karena hasilnya dapat terlihat dalam waktu 1 bulan, sedangkan merek B tidak membuahkan hasil sama sekali. 4. Evaluative Summation / Summary Yap, summary adalah kesimpulan. Jadi, setelah rampung menulis tiga struktur sebelumnya, pada bagian ini penulis dapat memberi kesimpulan sebagai akhir dari review text. Kesimpulan adalah opini terakhir dari penulis. Dalam hal ini, penulis bisa juga menambahkan kritik dan saran untuk owner, creator, atau author. Selain itu, bagian ini berfungsi untuk memberikan penegasan pada audiens apakah suatu produk/karya recommended untuk digunakan atau tidak. Apa Tujuan Dari Review Text? Purpose of Review Text Dalam menulis sebuah teks, pasti ada tujuan dan fungsi yang ingin dicapai, guys. Ini dia tujuan dari review text 1. Memberikan informasi baru berupa gambaran produk, evaluasi, dan kritik untuk pembaca atau khalayak umum. 2. Menjelaskan secara detail terkait kualitas, kelebihan, dan juga kekurangan yang ada pada suatu produk atau karya. Harapannya, audiens yang membaca review text bisa mendapat gambaran yang lebih rinci sebelum akhirnya mereka memutuskan untuk membeli atau menikmati suatu produk/karya. Contoh, kamu ingin membeli sebuah laptop A, tetapi bingung apakah laptop tersebut akan sesuai dengan kebutuhan atau tidak. Nah, kalau ada review text tentang laptop A, pasti kamu akan lebih terbantu, bukan? 3. Selain untuk khalayak umum, jenis teks yang satu ini akan berpengaruh terhadap creator, author, atau owner dari sebuah produk. Jika penulis memberikan review text yang bagus, maka secara tidak langsung mereka telah mempromosikan suatu karya atau produk secara Secara tidak langsung, review text dapat kamu jadikan media untuk pemberian input pada produk/publikasi yang dibuat oleh creator, author, atau owner. Pasalnya, jika ada penulis yang menuangkan kritik pada teks review, biasanya akan dilengkapi juga dengan saran. Hal ini tentu bisa menjadi bekal bagi para creator, author, dan owner agar mereka bisa menghasilkan sebuah produk/karya yang lebih baik lagi. Ciri-ciri Review Text Characteristics of Review Text Kira-kira, apa, sih, yang membedakan review text dengan jenis teks bahasa Inggris lainnya? 1. Berisi mengenai opini yang sifat subjektif tergantung bagaimana sudut pandang penulis personal. 2. Dapat memberikan preferensi kepada pembaca. Misal, Ratna mereview sebuah buku genre romantis dengan judul A. Namun, ternyata Ia memberikan bad review karena pada dasarnya Ratna lebih menyukai buku dengan genre petualangan. Hal ini tentu akan kembali pada pilihan pembaca, jadi review Ratna tidak sepenuhnya dapat diterima oleh orang lain. Language Features of Review Text Unsur Kebahasaan Review Text Bahasa Inggris erat kaitannya dengan grammar seperti tenses dan juga part of speech. Kalau dalam teks, biasa disebut juga sebagai unsur kebahasaan. Language features dalam review text terdiri dari 1. Using simple present tense Kalau nggak skip belajar simple present tense melalui artikel Simple Present Tense Pengertian, Kegunaan, Rumus, dan Contoh Kalimat, sepertinya menulis review text bukan hal yang sulit untukmu. Simple present tense adalah bentuk kalimat dengan kata kerja yang menunjukkan suatu aktivitas di masa sekarang. Karena tidak bersifat terikat oleh waktu, maka review text sangat cocok untuk menggunakan tenses yang satu ini. Contoh kalimatnya begini Aroskin Retinol Serum is a serum clear gel texture that feels thick on the skin. Even though it’s thick, it’s not heavy and tends to absorb quickly. In addition, this serum is fragrance-free so it doesn’t emit any scent. 2. Using adjectives Sudah mengenal salah satu part of speech yang satu ini belum? Kita sempat membahasnya bersama melalui artikel 7 Jenis Kata Sifat Adjective Bahasa Inggris Beserta Contohnya guys. Exactly, adjective adalah kata sifat. Umumnya, adjectives pada review text memiliki tujuan untuk memberikan gambaran tentang keadaan sebuah produk atau karya pada pembaca. Contoh dari adjectives seperti bad, good, etc. Kalau melihat contoh pada poin 1, yang termasuk adjective adalah thick, heavy, and fragrance-free. 3. Focus on specific participants Specific participant adalah sesuatu yang memiliki objek tertentu, ia tidak bersifat umum dan hanya ada satu. Nah, karena review text mengulas tentang suatu produk atau karya, tentunya objek yang dituju pun bersifat spesifik. Contohnya kamu ingin mengulas film Doctor Strange in the Multiverse of Madness, judul film tersebut tentu hanya ada satu, kan? Contoh lain adalah novel Laskar Pelangi karya Andrea Hirata, and many more. 4. Using long and complex clauses Actually, penjelasan apa itu long and complex clauses cukup panjang. Tapi intinya, complex clauses adalah kalimat yang terdiri atas satu independent clause serta satu atau lebih dependent clause/subordinate clause. Independent clause adalah inti kalimat yang bisa berdiri sendiri. Sementara itu, dependent clause adalah klausa yang bergantung pada kalimat inti. Nah, biasanya kedua klausa ini dipisahkan dengan tanda koma , untuk membentuk sebuah complex clauses. Selain itu, ditandai juga dengan conjunction atau kata penghubung. Kita coba ambil kalimat dari poin nomor satu Even though the texture of aroskin retinol serum is thick, it’s not heavy and tends to absorb quickly. Pada contoh di atas, terdapat konjungsi even though yang artinya “meski” dengan fungsi menunjukkan pertentangan. Umumnya, tekstur cairan yang kental akan sulit untuk meresap pada kulit, akan tetapi serum dari Aroskin mampu menyerap dengan cukup cepat. 5. Using metaphor or idiom Masih ingat dengan majas? Yap, metaphor atau metafora adalah salah satu jenis majas. Mengutip dari KBBI, metafora adalah kata atau kelompok kata dengan arti yang bukan sebenarnya, melainkan sebagai lukisan yang berdasarkan persamaan atau perbandingan. Misalnya seperti ini Aroskin claims that their retinol serum product can brighten the face within 1 month. I think it’s wrong, because the serum can make my face compete with shiny glass in just 2 weeks. Tujuan dari metaphor tersebut adalah untuk membuat teks lebih enak dibaca, sekaligus bisa mempengaruhi audiens bahwa serum yang diulas memang layak untuk dicoba. Sementara itu, idiom adalah serangkaian kata yang tidak bisa diartikan secara harfiah. Kamu bisa cek penjelasan lengkapnya di artikel 101 Idioms yang Tidak Bisa Diterjemahkan Secara Harfiah ya! Contohnya adalah sebagai berikut I think this music can make a lot of people feel as snug as a bug in a rug. Kalau secara harfiah, snug as a bug in a rug artinya adalah nyaman seperti serangga di permadani. Padahal, makna yang sebenarnya adalah “senang/nyaman” Apa Perbedaan Review Text dan Resensi? Mungkin kamu sedikit bingung, apa perbedaan review text dan juga resensi. Jika menyimpulkan dari berbagai sumber, review text adalah teks dalam bahasa Inggris yang berfungsi untuk mengulas mulai dari barang, jasa, atau karya. Berbeda dengan resensi yang identik dengan ulasan terhadap suatu karya saja seperti film, buku, atau lagu. Selain itu, dalam resensi biasanya hanya berfokus pada satu produk, alias tidak membandingkan dengan produk lain. Contoh Review Text Example of Review Text Review of Top Gun Maverick Film Introduction Top Gun Maverick is a 2022 American action drama film directed by Joseph Kosinski and written by Ehren Kruger, Eric Warren Singer and Christopher McQuarrie, from a story by Peter Craig and Justin Marks. The sequel to Top Gun 1986, the film stars Tom Cruise as Captain Pete “Maverick” Mitchell reprising his role from the original, alongside Miles Teller, Jennifer Connelly, Jon Hamm, Glen Powell, Lewis Pullman, Ed Harris, and Val Kilmer. Top Gun Maverick released in the box office on May 27, 2022 by Paramount Pictures. Evaluation This film tells the story of the great naval aviator of more than 30 years, Pete Mitchell who is assigned to lead his juniors on a mission. Pete only has less than three weeks to make all his juniors have the skills and strong mental to carry out the mission. The mission that must be carried out is quite dangerous, so the juniors must have jet maneuvering skills and also high concentration. The action of chasing fighter jets in the air is one of the stunning displays and the main attraction of this film. The most unique fact of this film is about Tom Cruise operating a real fighter jet. At first, he was skeptical about this project, but the production team twisted Tom Cruise’s arm by sending him to the Naval Air Facility in El Centro California to ride an F-14 until finally he was convinced to take this film. Interpretative Reportedly, Top Gun Maverick managed to earn Rp 11 trillion and became Tom Cruise’s most successful film. I think they really deserve it. This film not only emphasizes the skill side as a pilot, but there are stories of family, love, friendship, competition, and friendship. This film deserves to be the best film in 2022. Summary If you are a person who likes action or adventure genre films, then this film will amaze you. Tom Cruise’s expertise in “playing” fighter jets amazed the audience. You will have a great experience if you watch it in a 4D cinema. Penjelasan Dari contoh di atas, berikut analisa terkait kaidah kebahasaan yang digunakan ditunjukkan dengan bold 1. Simple present tense Top Gun Maverick is a 2022 American action drama film This film tells the story of the great naval aviator of more than 30 years 2. Using adjectives Have the skills and strong mental to carry out the mission The mission that must be carried out is quite dangerous 3. Using specific participant Top Gun Maverick is a 2022 American action drama film Top Gun Maverick released in the box office on May 27, 2022 by Paramount Pictures Naval Air Facility in El Centro California 4. Using long and complex causes At first, he was skeptic about this project, but the production team twisted Tom Cruise’s arm by sending him to the Naval Air Facility in El Centro California 5. Using metaphor and idioms The production team twisted Tom Cruise’s arm by sending him to the Naval Air Facility in El Centro California Finally, itulah penjelasan mengenai review text yang cukup lengkap. Jadi, kalau kamu ingin menulis ulasan tentang produk atau karya tapi nggak tahu bagaimana caranya, ikuti saja langkah-langkah di artikel ini ya! Ingin lebih mahir writing dalam bahasa Inggris? Yuk, belajar di English Academy bareng pengajar lokal dan internasional! Ketahui dulu bagaimana proses kelasnya melalui Free Trial Class dengan cara klik gambar di bawah ini ya!
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language features of review text