A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating. <s> BIB001 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> We identify a new task in the ongoing research in text sentiment analysis: predicting utility of product reviews, which is orthogonal to polarity classification and opinion extraction. We build regression models by incorporating a diverse set of features, and achieve highly competitive performance for utility scoring on three real-world data sets. <s> BIB002 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums. <s> BIB003 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. ::: ::: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. <s> BIB004 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> Online reviews provide a valuable resource for potential customers to make purchase decisions. However, the sheer volume of available reviews as well as the large variations in the review quality present a big impediment to the effective use of the reviews, as the most helpful reviews may be buried in the large amount of low quality reviews. The goal of this paper is to develop models and algorithms for predicting the helpfulness of reviews, which provides the basis for discovering the most helpful reviews for given products. We first show that the helpfulness of a review depends on three important factors: the reviewerpsilas expertise, the writing style of the review, and the timeliness of the review. Based on the analysis of those factors, we present a nonlinear regression model for helpfulness prediction. Our empirical study on the IMDB movie reviews dataset demonstrates that the proposed approach is highly effective. <s> BIB005 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> With the number of online reviews growing rapidly, it is increasingly difficult to digest all the information within limited time. To help users efficiently get concise information about a product, researchers have studied algorithms for automated opinion summarization. However, users might expect to further read detailed high-quality reviews in addition to a review outline. This raises another interesting problem not well studied yet: how to discover high quality product reviews? Previous research examined various properties of a product review to predict its quality. In this paper, we further explore this topic by incorporating another information resource: the behavior of review authors in an e-commerce community. First, we perform a high-level analysis on two kinds of data: product reviews and deal transactions. According to the results of this analysis, three features, including personal reputation, seller degree and expertise degree, are studied to assess the quality of a review from a credibility and expertise perspective. Our analysis shows that these features are strongly related to review quality and that they can help uncover review spamming by sellers. Furthermore, we propose a simulation model based on the above findings. The model is able to generate the basic properties of the review community, especially when the above three features are taken into account. <s> BIB006 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are constantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors' identities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of review quality prediction especially when the available training data is sparse. <s> BIB007 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> Writing and publishing reviews online has become an increasingly popular way for people to express opinions and sentiments. Analyzing the large volume of online reviews available can produce useful knowledge that are of interest to vendors and other parties. Prior studies in the literature have shown that online reviews have a significant correlation with the sales of products, and therefore mining the reviews could help predict the sales performance of relevant products. However, those studies fail to consider one important factor that may significantly affect the accuracy of the prediction, i.e., the quality of the reviews. In this paper, we propose a regression model that explicitly takes into account the quality factor, and discusses how this quality information can be predicted when it is not readily available. Experimental results on a movie review dataset confirm the effectiveness of the proposed model. <s> BIB008 </s> A Survey of Quality Prediction of Product Reviews <s> III. QUALITY AS REGRESSION PROBLEM <s> With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact. <s> BIB009
Generally, the problem of determining the quality of reviews is seen as a regression problem. The method uses machine learning models to assign a score to each review. These scores can be used in recommendation and ranking systems  . Researchers have used different types of features to train and test models on datasets from different domains. A Support Vector Machine (SVM) regression model to rank reviews according to their helpfulness was employed by BIB001 . They used structural features (e.g., review length, number of sentences), lexical features (e.g., unigrams and bigrams), syntactic features (e.g., nouns, verbs, etc.), semantic features (e.g. sentiment words) and meta-data features (e.g., number of stars)  . The most useful features used were the length of the review, its unigram and its product rating. Zhang and Varadarajan BIB002 use a similar feature set to that proposed in BIB001 . However, they did not include any meta-data information. Their study assumes that a good quality review should discuss many aspects of the product. Thus, a comparison between the review and the product specifications was considered. However, the results show that this feature did not improve system performance BIB004 . Furthermore, the study includes review similarity to editorial reviews, which did not improve the system performance either. They found that the perceived helpfulness in product reviews depends greatly on its linguistic style (e.g., word count, comparatives and superlatives, proper nouns, etc). In contrast with the results of BIB001 , there is a weak correlation between review length and utility score BIB004 BIB002 . According to Pang and Lee BIB004 , the difference in domain choice affected the results in the two studies. Electronic product reviews used in BIB001 do not include as sophisticated language as found in book and movie reviews, which were used in BIB002 . A different approach was proposed by Ghose and Ipeirotis BIB003 , who studied the relationship between the subjectivity of a review and its helpfulness. A classifier determines the subjectivity of a sentence, and then the standard deviation of the subjectivity score of the sentences in a given review is computed. The results indicate that the standard deviation score and a readability score have a strong effect on utility evaluation. Building on their previous research, Ghose and Ipeirotis BIB009 expanded their work by examining multiple product categories and by adding textual features, such as history information about the author, readability metrics and spelling errors, etc. They found that reviews including a mixture of subjective and objective information influence sales and the perceived usefulness. In addition, readability and informativeness features were found to correlate positively with sales and the perceived usefulness. An important finding of this research is that the type of product affects the perceived helpfulness of a review. For feature-based products (e.g., electronics), reviews that include objective more than subjective information increase the usefulness of the review. However, in experience products (e.g., movies), it was found that subjectivity matters the most, as users prefer to read personalized and highly sentimental comments that describe the reviewer experience and provide more information about the product. Looking at the problem from a different perspective than the above approaches, the work in BIB005 introduced three main factors affecting the helpfulness of a review: reviewer's expertise, review timeline and review style based on part-ofspeech tags. A nonlinear regression model was used to integrate the proposed factors. Extensive experiments on movie reviews (IMDB data-set) show the efficiency of the proposed model. They argue that their model is general enough to be employed in other domains, by replacing the genres of movies with the categories of products and by modeling the timelines and the writing style using their proposed algorithm. Previous research efforts focus only on the meta-data and on the review text itself to analyze various properties of product reviews in order to predict quality. Other studies tried to tackle the quality problem from different perspectives. www.ijacsa.thesai.org For example, the study by BIB006 incorporates another information resource: the author's behaviour on e-commerce sites, such that information derived from their online transactions helped to assess the quality of reviews and to identify spammed ones. Three features were used to assess the quality of reviews: personal reputation, seller degree and expertise degree. The correlation between each feature and the helpfulness votes was examined using a linear regression analysis. Lu, Tsaparas, Ntoulas, and Polanyi BIB007 , investigate if the social context of reviews can enhance the performance of quality prediction. In their view, important information can be obtained from the social context about the quality of reviewers, which affects the quality of their reviews. In order to incorporate social context in predicting review quality, regularization constraints, based on a set of experimentallyvalidated hypotheses, were employed. An example is the "author constraint hypothesis", which assumes that reviews from the same author are similar in quality BIB007 . The results show accuracy improvement in predicting the quality of reviews using a text-based classifier (linear regression model). They argue that the proposed regularization technique can be applicable and generalized for quality evaluation of other user generated content. However, this method cannot be employed to review sites that do not have a trusted social network  . Previous studies have proven that online reviews affect the sales of products, however many studies fail to consider the quality of reviews. Another group of interested researchers BIB008 proposed a regression model that incorporates the quality factor for predicting sales performance of products being reviewed. Results indicate the positive correlation between review quality and prediction accuracy of sales performance.