Wordcloud of the sentiment analysis article on Wikipedia. Sentiment analysis aims to estimate the sentiment polarity of a body of text based solely on its content. The sentiment polarity of text can be defined as a value that says whether the expressed opinion is positive (polarity=1), negative (polarity=0), or neutral.In this tutorial, we will assume that texts are either positive or THELANGUAGE SPACE. Reading&Research; The Ultimate Research Writing Tool; TEXT TYPES FOR ENGLISH B You will need to learn to be able to use a variety of text features effectively when you produce the text types listed below. Knowing how to write these different text types is important in Paper Two, the writing paper, and also when you Reviewof Text Structures and Language Features Text Structure: The pattern a author uses to structure the ideas in a text. Language Features: The techniques used to add meaning and interest to their work. Common Language Features Write a recount of an event in your life using 3 Thepurpose of the text feature below the photo is A) To show the main idea of the article to the reader. B) To convince the reader that snakes are dangerous C) To provide an explanation of the photo D) To inform the reader about the history of snakes. Thursday, February 16, 2012. 7. What is the text Introduction Sentiment analysis is part of the Natural Language Processing (NLP) techniques that consists in extracting emotions related to some raw texts. This is usually used on social media posts and customer reviews in order to automatically understand if some users are positive or negative and why. Vay Tiền Trả Góp 24 Tháng. 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 Expression in a literature review should be informative and evaluative. Apart from incorporating reporting verbs, you will need to use evaluative and cautious verbsA key language feature of a literature review is the use of reporting verbs. These types of verbs describe and report on the literature under review. They report onaims investigates, examines, looks atresults shows, suggests, revealsopinions states, believes, arguesThe choice of reporting verbs indicates your perspectives and attitudes towards the research under review. That is the reporting verbs chosen show whether you are neutral, negative or positive about the sentence pattern of placing reporting verbs is [reporting verb] + either/both [object] / [complement].Evaluative and cautious languageYou can show your perspective on the literature under review by using evaluative language. Evaluative language can indicate whether you’re positive or negative towards the claims in the literature, whether you agree or disagree with the claims language is careful not to express absolute certainty where there may be the possibility of language can bepositive expressions like “effective,” “necessary,” “significant” or “crucial”negative “questionable,” unclear,” “inconclusive” or evaluation Wright’s 2022 argument about the link between parental numeracy and that of their children is conclusively borne out in the evaluation Whether the statistical results support Torney and Wittings’ 2021 hypothesis is way to express certainty or hesitancy is to use boosters and are words or phrases that express confidence or certaintyhedges convey a qualified uncertainty in the claims made in the on the tabs below to view examples of evaluative language, boosters and hedges. ReferencesBailey, Stephen. 2015. The Essentials of Academic Writing for International Students. Taylor & Francis . & Volpe, M. 2012. Completing your qualitative dissertation A road map from beginning to end 2nd ed.. Sage & Ruth, R. 2018. Writing the Literature Review A Practical Guide. Guilford B. & Thomson, P. 2006. Helping Doctoral Students Write. Pedagogies for Supervision. K. E., & Newton, R. R. 1992. Surviving your dissertation A comprehensive guide to content and process. Sage Publications. 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 WHAT IS A BOOK REVIEW? Traditionally, book reviews are written evaluations of a recently published book in any genre. Usually, around the 500 to 700-word mark, they offer a brief description of a text’s main elements while appraising the work’s strengths and weaknesses. Published book reviews can appear in newspapers, magazines, and academic journals. They provide the reader with an overview of the book itself and indicate whether or not the reviewer would recommend the book to the reader. WHAT IS THE PURPOSE OF A BOOK REVIEW? There was a time when book reviews were a regular appearance in every quality newspaper and many periodicals. They were essential elements in whether or not a book would sell well. A review from a heavyweight critic could often be the deciding factor in whether a book became a bestseller or a damp squib. In the last few decades, however, the book review’s influence has waned considerably, with many potential book buyers preferring to consult customer reviews on Amazon, or sites like Goodreads, before buying. As a result, book review’s appearance in newspapers, journals, and digital media has become less frequent. WHY BOTHER TEACHING STUDENTS TO WRITE BOOK REVIEWS AT ALL? Even in the heyday of the book review’s influence, few students who learned the craft of writing a book review became literary critics! The real value of crafting a well-written book review for a student does not lie in their ability to impact book sales. Understanding how to produce a well-written book review helps students to ● Engage critically with a text ● Critically evaluate a text ● Respond personally to a range of different writing genres ● Improve their own reading, writing, and thinking skills. Not to Be Confused with a Book Report! WHAT’S THE DIFFERENCE BETWEEN A BOOK REVIEW AND A BOOK REPORT? While the terms are often used interchangeably, there are clear differences in both the purpose and the format of the two genres. Generally speaking, book reports aim to give a more detailed outline of what occurs in a book. A book report on a work of fiction will tend to give a comprehensive account of the characters, major plot lines, and themes in the book. Book reports are usually written around the K-12 age range, while book reviews tend not to be undertaken by those at the younger end of this age range due to the need for the higher-level critical skills required in writing them. At their highest expression, book reviews are written at the college level and by professional critics. Learn how to write a book review step by step with our complete guide for students and teachers by familiarizing yourself with the structure and features. BOOK REVIEW STRUCTURE ANALYZE Evaluate the book with a critical mind. THOROUGHNESS The whole is greater than the sum of all its parts. Review the book as a WHOLE. COMPARE Where appropriate compare to similar texts and genres. THUMBS UP OR DOWN? You are going to have to inevitably recommend or reject this book to potential readers. BE CONSISTENT Take a stance and stick with it throughout your review. FEATURES OF A BOOK REVIEW PAST TENSE You are writing about a book you have already read. EMOTIVE LANGUAGE Whatever your stance or opinion be passionate about it. Your audience will thank you for it. VOICE Both active and passive voice are used in recounts. A COMPLETE UNIT ON REVIEW AND ANALYSIS OF TEXTS ⭐ Make MOVIES A MEANINGFUL PART OF YOUR CURRICULUM with this engaging collection of tasks and tools your students will love. ⭐ All the hard work is done for you with NO PREPARATION REQUIRED. This collection of 21 INDEPENDENT TASKS and GRAPHIC ORGANIZERS takes students beyond the hype, special effects and trailers to look at visual literacy from several perspectives offering DEEP LEARNING OPPORTUNITIES by watching a SERIES, DOCUMENTARY, FILM, and even VIDEO GAMES. ELEMENTS OF A BOOK REVIEW As with any of the writing genres we teach our students, a book review can be helpfully explained in terms of criteria. While there is much to the art’ of writing, there is also, thankfully, a lot of the nuts and bolts that can be listed too. Have students consider the following elements before writing ● Title Often, the title of the book review will correspond to the title of the text itself, but there may also be some examination of the title’s relevance. How does it fit into the purpose of the work as a whole? Does it convey a message or reveal larger themes explored within the work? ● Author Within the book review, there may be some discussion of who the author is and what they have written before, especially if it relates to the current work being reviewed. There may be some mention of the author’s style and what they are best known for. If the author has received any awards or prizes, this may also be mentioned within the body of the review. ● Genre A book review will identify the genre that the book belongs to, whether fiction or nonfiction, poetry, romance, science-fiction, history etc. The genre will likely tie in, too with who the intended audience for the book is and what the overall purpose of the work is. ● Book Jacket / Cover Often, a book’s cover will contain artwork that is worthy of comment. It may contain interesting details related to the text that contribute to, or detract from, the work as a whole. ● Structure The book’s structure will often be heavily informed by its genre. Have students examine how the book is organized before writing their review. Does it contain a preface from a guest editor, for example? Is it written in sections or chapters? Does it have a table of contents, index, glossary etc.? While all these details may not make it into the review itself, looking at how the book is structured may reveal some interesting aspects. ● Publisher and Price A book review will usually contain details of who publishes the book and its cost. A review will often provide details of where the book is available too. WHEN WRITING A BOOK REVIEW YOUR GOAL IS TO GO BEYOND SIMPLY SCRATCHING THE SURFACE AND MAKE A DEEP ANALYSIS OF A TEXT. BOOK REVIEW KEY ELEMENTS As students read and engage with the work they will review, they will develop a sense of the shape their review will take. This will begin with the summary. Encourage students to take notes during the reading of the work that will help them in writing the summary that will form an essential part of their review. Aspects of the book they may wish to take notes on in a work of fiction may include ● Characters Who are the main characters? What are their motivations? Are they convincingly drawn? Or are they empathetic characters? ● Themes What are the main themes of the work? Are there recurring motifs in the work? Is the exploration of the themes deep or surface only? ● Style What are the key aspects of the writer’s style? How does it fit into the wider literary world? ● Plot What is the story’s main catalyst? What happens in the rising action? What are the story’s subplots? A book review will generally begin with a short summary of the work itself. However, it is important not to give too much away, remind students – no spoilers, please! For nonfiction works, this may be a summary of the main arguments of the work, again, without giving too much detail away. In a work of fiction, a book review will often summarise up to the rising action of the piece without going beyond to reveal too much! The summary should also provide some orientation for the reader. Given the nature of the purpose of a review, it is important that students’ consider their intended audience in the writing of their review. Readers will most likely not have read the book in question and will require some orientation. This is often achieved through introductions to the main characters, themes, primary arguments etc. This will help the reader to gauge whether or not the book is of interest to them. Once your student has summarized the work, it is time to review’ in earnest. At this point, the student should begin to detail their own opinion of the book. To do this well they should i. Make It Personal Often when teaching essay writing we will talk to our students about the importance of climbing up and down the ladder of abstraction. Just as it is helpful to explore large, more abstract concepts in an essay by bringing it down to Earth, in a book review, it is important that students can relate the characters, themes, ideas etc to their own lives. Book reviews are meant to be subjective. They are opinion pieces, and opinions grow out of our experiences of life. Encourage students to link the work they are writing about to their own personal life within the body of the review. By making this personal connection to the work, students contextualize their opinions for the readers and help them to understand whether the book will be of interest to them or not in the process. ii. Make It Universal Just as it is important to climb down the ladder of abstraction to show how the work relates to individual life, it is important to climb upwards on the ladder too. Students should endeavor to show how the ideas explored in the book relate to the wider world. The may be in the form of the universality of the underlying themes in a work of fiction or, for example, the international implications for arguments expressed in a work of nonfiction. iii. Support Opinions with Evidence A book review is a subjective piece of writing by its very nature. However, just because it is subjective does not mean that opinions do not need to be justified. Make sure students understand how to back up their opinions with various forms of evidence, for example, quotations, statistics, and the use of primary and secondary sources. EDIT AND REVISE YOUR BOOK REVIEW As with any writing genre, encourage students to polish things up with review and revision at the end. Encourage them to proofread and check for accurate spelling throughout, with particular attention to the author’s name, character names, publisher etc. It is good practice too for students to double-check their use of evidence. Are statements supported? Are the statistics used correctly? Are the quotations from the text accurate? Mistakes such as these uncorrected can do great damage to the value of a book review as they can undermine the reader’s confidence in the writer’s judgement. The discipline of writing book reviews offers students opportunities to develop their writing skills and exercise their critical faculties. Book reviews can be valuable standalone activities or serve as a part of a series of activities engaging with a central text. They can also serve as an effective springboard into later discussion work based on the ideas and issues explored in a particular book. Though the book review does not hold the sway it once did in the mind’s of the reading public, it still serves as an effective teaching tool in our classrooms today. Teaching Resources Use our resources and tools to improve your student’s writing skills through proven teaching strategies. BOOK REVIEW GRAPHIC ORGANIZER TEMPLATE 101 DIGITAL & PRINT GRAPHIC ORGANIZERS FOR ALL CURRICULUM AREAS Introduce your students to 21st-century learning with this GROWING BUNDLE OF 101 EDITABLE & PRINTABLE GRAPHIC ORGANIZERS. ✌NO PREP REQUIRED!!!✌ Go paperless, and let your students express their knowledge and creativity through the power of technology and collaboration inside and outside the classroom with ease. Whilst you don’t have to have a 11 or BYOD classroom to benefit from this bundle, it has been purpose-built to deliver through platforms such as ✔ GOOGLE CLASSROOM, ✔ OFFICE 365, ✔ or any CLOUD-BASED LEARNING PLATFORM. Book and Movie review writing examples Student Writing Samples Below are a collection of student writing samples of book reviews. Click on the image to enlarge and explore them in greater detail. Please take a moment to both read the movie or book review in detail but also the teacher and student guides which highlight some of the key elements of writing a text review Please understand these student writing samples are not intended to be perfect examples for each age or grade level but a piece of writing for students and teachers to explore together to critically analyze to improve student writing skills and deepen their understanding of book review writing. We would recommend reading the example either a year above and below, as well as the grade you are currently working with to gain a broader appreciation of this text type. Year 3Year 4Year 5Year 6Year 7Year 8 OTHER GREAT ARTICLES RELATED TO BOOK REVIEWS The content for this page has been written by Shane Mac Donnchaidh. A former principal of an international school and English university lecturer with 15 years of teaching and administration experience. Shane’s latest Book, The Complete Guide to Nonfiction Writing, can be found here. Editing and support for this article have been provided by the literacyideas team.

language features of review text