A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> A model of interpersonal terms (verbs and adjectives) is reviewed in terms of the research on: (a) the systematic cognitive inferences these terms mediate, and (b) the implications of this model for social cognitive processes as it is applied in different domains such as attribution processes and intergroup relations. <s> BIB001 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework. <s> BIB002 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> We conducted the first randomized controlled field experiment of an Internet reputation mechanism. A high-reputation, established eBay dealer sold matched pairs of lots—batches of vintage postcards—under his regular identity and under new seller identities (also operated by him). As predicted, the established identity fared better. The difference in buyers’ willingness-to-pay was 8.1% of the selling price. A subsidiary experiment followed the same format, but compared sales by relatively new sellers with and without negative feedback. Surprisingly, one or two negative feedbacks for our new sellers did not affect buyers’ willingness-to-pay. Copyright Economic Science Association 2006 <s> BIB003 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. <s> BIB004 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. <s> BIB005 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Analysis of usage history for a large panel of eBay sellers suggests that both seller and buyer behavior change in response to changes in a seller's feedback profile. Sellers are more likely to stop listing items right after receiving a negative feedback. Sellers who continue listing do not seem to improve their performance in order to salvage their reputations. Instead, sellers get more negative feedback after receiving a negative feedback. One reason is that observed negative feedback appears to be symptomatic of a temporary decline in the seller's quality, which is also reflected in other transactions around the same time. Receipt of negative feedback might also cause a decline in seller quality, but we find only weak evidence of that. Empirical evidence does support a second hypothesis, that buyers appear to be more willing to give negative feedback to sellers who have recently received other negative feedback. <s> BIB006 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB007 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB008 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> The e-rater® system has been used by ETS for automated essay scoring since 1999. This paper describes a new version of e-rater (v.2.0) that differs from the previous one (v.1.3) with regard to the feature set and model building approach. The paper describes the new version, compares the new and previous versions in terms of performance, and presents evidence on the validity and reliability of scores produced by the new version. <s> BIB009 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline. <s> BIB010 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB011 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Product reviews posted at online shopping sites vary greatly in quality. This paper addresses the problem of detecting lowquality product reviews. Three types of biases in the existing evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of specifications for judging the quality of reviews is first defined. A classificationbased approach is proposed to detect the low-quality reviews. We apply the proposed approach to enhance opinion summarization in a two-stage framework. Experimental results show that the proposed approach effectively (1) discriminates lowquality reviews from high-quality ones and (2) enhances the task of opinion summarization by detecting and filtering lowquality reviews. <s> BIB012 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Assessing the quality of user generated content is an important problem for many web forums. While quality is currently assessed manually, we propose an algorithm to assess the quality of forum posts automatically and test it on data provided by Nabble.com. We use state-of-the-art classification techniques and experiment with five feature classes: Surface, Lexical, Syntactic, Forum specific and Similarity features. We achieve an accuracy of 89% on the task of automatically assessing post quality in the software domain using forum specific features. Without forum specific features, we achieve an accuracy of 82%. <s> BIB013 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Assessing the quality of user generated content is an important problem of Web 2.0. Currently, most web sites need their users to rate content manually, which is labour intensive and thus happens rarely. The automatic systems in the literature are limited to one kind or domain of discourse. We propose a system to assess the quality of user generated discourse automatically. Our system learns from human ratings by applying SVM classification based on features such as Surface, Lexical, Syntactic, Forum specific and Similarity features. Our system has also shown to be adaptable to different domains of discourse in our experiments on three different web forum data sets. The system outperformed the majority class baseline for all three data sets. Our best performing system configuration achieves an accuracy of 89.1%, which is significantly higher than the baseline of 61.82%. <s> BIB014 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Previous methods usually conduct the keyphrase extraction task for single documents separately without interactions for each document, under the assumption that the documents are considered independent of each other. This paper proposes a novel approach named CollabRank to collaborative single-document keyphrase extraction by making use of mutual influences of multiple documents within a cluster context. CollabRank is implemented by first employing the clustering algorithm to obtain appropriate document clusters, and then using the graph-based ranking algorithm for collaborative single-document keyphrase extraction within each cluster. Experimental results demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the system performance relies positively on the quality of document clusters. <s> BIB015 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB016 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> In this paper, we propose a model for evaluating the quality of general user-created documents. The model is based on supervised classification approach, in which output scores are considered as quality of given document. In order to utilize both textual and nontextual attributes of documents, we incorporated a number of objectively measurable, real-valued features selected upon predefined criteria for quality. Experiments on two datasets of real world documents show that textual features are stable indicators for evaluating documents' quality. Some features are inferred to be effective for general kinds of documents. <s> BIB017 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans <s> BIB018 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Online product review networks help to transmit information that customers can use to evaluate product quality. The prior literature has found that, in the aggregate, better product reviews lead to higher sales. However, product review networks increasingly include an explicit social component that allows consumers to evaluate individual reviews based on the status of the reviewer and on the "helpfulness" of the review to the community. In this research, we extend this literature by analyzing the impact of reviews at a disaggregate level. We find that reviews that the community finds helpful have a stronger influence on consumers' purchase decisions than other reviews do. Moreover, these reviews have a stronger impact on less popular books than on more popular books, where consumers may be able to use other outside information sources to form an opinion of the product. Overall, our results suggest that the micro-level dynamics of community interactions are valuable in signaling quality over-and-above aggregate-level scores. One implication of this result is that the micro-level dynamics of reputation communities make it harder for self-interested parties to manipulate reviews versus an environment where consumers only have aggregate quality measures. <s> BIB019 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB020 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> User-generated reviews are a common and valuable source of product information, yet little attention has been paid as to how best to present them to end-users. In this paper, we describe a classification-based recommender system that is designed to recommend the most helpful reviews for a given product. We present a large-scale evaluation of our approach using TripAdvisor hotel reviews, and we show that our approach is capable of suggesting superior reviews compared to a number of alternative recommendation benchmarks. <s> BIB021 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> With the rapid growth of e-commerce, there are a great number of customer reviews on the e-commerce websites. Generally, potential customers usually wade through a lot of on-line reviews in order to make an informed decision. However, retrieving sentiment information relevant to customer's interest still remains challenging. Developing a sentiment mining and retrieval system is a good way to overcome the problem of overloaded information in customer reviews. In this paper, we propose a sentiment mining and retrieval system which mines useful knowledge from consumer product reviews by utilizing data mining and information retrieval technology. A novel ranking mechanism taking temporal opinion quality (TOQ) and relevance into account is developed to meet customers' information need. Besides the trend movement of customer reviews and the comparison between positive and negative evaluation are presented visually in the system. Experimental results on a real-world data set show the system is feasible and effective. <s> BIB022 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from ifferent countries. <s> BIB023 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods. <s> BIB024 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Combining econometric analysis with text mining techniques, this study attempts to explore why some online product reviews have no usefulness rating through examining review posting time and text features. Later posting time may reduce the probability of some online reviews being seen and thus lead to their being not rated for usefulness. Besides, the neutral diagnosticity of reviews reflected from the text features may cause difficulty for readers to judge and evaluate the usefulness of these reviews. Our study finds that, though not being seen due to later posting time obviously explains no usefulness rating for some online reviews, the neutral diagnosticity of these reviews is also an important and non-neglectable cause for their lack of usefulness rating. Further, we identify the text features which may lead to the neutral diagnosticity of the review. Our study has implications for online product reviews website managers in identifying and dismissing the reviews with no usefulness rating to improve readers’ information retrieving efficiency and also for reviewers in improving the quality of their reviews. <s> BIB025 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB026 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> In the Web 2.0 era, internet users contribute a large amount of online content. Product review is a good example. Since these phenomena are distributed all over shopping sites, weblogs, forums etc., most people have to rely on general search engines to discover and digest others' comments. While conventional search engines work well in many situations, it's not sufficient for users to gather such information. The reasons include but are not limited to: 1) the ranking strategy does not incorporate product reviews' inherent characteristics, e.g., sentiment orientation; 2) the snippets are neither indicative nor descriptive of user opinions. In this paper, we propose a feasible solution to enhance the experience of product review search. Based on this approach, a system named "Improved Product Review Search (IPRS)" is implemented on the ground of a general search engine. Given a query on a product, our system is capable of: 1) automatically identifying user opinion segments in a whole article; 2) ranking opinions by incorporating both the sentiment orientation and the topics expressed in reviews; 3) generating readable review snippets to indicate user sentiment orientations; 4) easily comparing products based on a visualization of opinions. Both results of a usability study and an automatic evaluation show that our system is able to assist users quickly understand the product reviews within limited time. <s> BIB027 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this research, we address this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers. Despite their popularity, it is well known that answers in community QA portals have unequal quality. We therefore propose a quality-aware framework to design methods that select answers from a community QA portal considering answer quality in addition to answer relevance. Besides using answer features for determining answer quality, we introduce several other quality-aware QA methods using answer quality derived from the expertise of answerers. Such expertise can be question independent or question dependent. We evaluate our proposed methods using a database of 95K questions and 537K answers obtained from Yahoo! Answers. Our experiments have shown that answer quality can improve QA performance significantly. Furthermore, question dependent expertise based methods are shown to outperform methods using answer features only. It is also found that there are also good answers not among the best answers identified by Yahoo! Answers users. <s> BIB028 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Given an extensive corpus of reviews on an item, a potential customer goes through the expressed opinions and collects information, in order to form an educated opinion and, ultimately, make a purchase decision. This task is often hindered by false reviews, that fail to capture the true quality of the item's attributes. These reviews may be based on insufficient information or may even be fraudulent, submitted to manipulate the item's reputation. In this paper, we formalize the Confident Search paradigm for review corpora. We then present a complete search framework which, given a set of item attributes, is able to efficiently search through a large corpus and select a compact set of high-quality reviews that accurately captures the overall consensus of the reviewers on the specified attributes. We also introduce CREST (Confident REview Search Tool), a user-friendly implementation of our framework and a valuable tool for any person dealing with large review corpora. The efficacy of our framework is demonstrated through a rigorous experimental evaluation. <s> BIB029 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor, given the rewards that can be derived from posting false or biased reviews. In this paper we present a number of criteria that might be indicative of suspicious reviews and evaluate alternative methods for integrating these criteria to produce a unified "suspiciousness" ranking. The criteria derive from characteristics of the network of reviewers and also from analysis of the content and impact of reviews and ratings. The integration methods that are evaluated are singular value decomposition and the unsupervised hedge algorithm. These alternatives are evaluated in a user study on TripAdvisor reviews, where volunteers were asked to rate the suspiciousness of reviews that have been highlighted by the criteria. <s> BIB030 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> In the Web 2.0 era, there has been an explosive growth of user-contributed data on the Web. Among the user-contributed data, the sheer volume of online reviews (or comments) provide enterprise with invaluable market intelligence about potential customers' preferences for various products and services. However, there has been growing concerns about the quality of these uncontrolled user-contributed online reviews. Despite numerous research work has been conducted on opinion mining and opinion retrieval, little work has been done to develop effective quality metrics to assess the quality of opinionated expressions. To discover rich and accurate business intelligence from online opinionated expressions, an objective quality-based filtering process is essential for any opinion mining systems. The main contribution of this paper is the design, development, and evaluation of a novel multi-facet quality metric for the assessment of the informativeness of opinionated expressions such as online product reviews. Our preliminary experiments show that the proposed multi-facets quality metric is more effective than a quality assessment approach constructed based on user-generated helpful votes. <s> BIB031 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB032 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <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> BIB033 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> The ubiquity of Web2.0 makes the Web an invaluable source of business information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable enterprises to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining and retrieval techniques are needed to identify important reviews and opinions to answer users' queries. Most opinion mining and retrieval approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the process often ignores the quality of each review and may retrieve useless or even noisy documents. In this paper, we propose a method for evaluating the quality of information in product reviews. We treat the evaluation of review quality as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly. <s> BIB034 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> The ''helpfulness'' feature of online user reviews helps consumers cope with information overloads and facilitates decision-making. However, many online user reviews lack sufficient helpfulness votes for other users to evaluate their true helpfulness level. This study empirically examines the impact of the various features, that is, basic, stylistic, and semantic characteristics of online user reviews on the number of helpfulness votes those reviews receive. Text mining techniques are employed to extract semantic characteristics from review texts. Our findings show that the semantic characteristics are more influential than other characteristics in affecting how many helpfulness votes reviews receive. Our findings also suggest that reviews with extreme opinions receive more helpfulness votes than those with mixed or neutral opinions. This paper sheds light on the understanding of online users' helpfulness voting behavior and the design of a better helpfulness voting mechanism for online user review systems. <s> BIB035 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Online user reviews play a central role in the decision-making process of users for a variety of tasks, ranging from entertainment and shopping to medical services. As user-generated reviews proliferate, it becomes critical to have a mechanism for helping the users (information consumers) deal with the information overload, and presenting them with a small comprehensive set of reviews that satisfies their information need. This is particularly important for mobile phone users, who need to make decisions quickly, and have a device with limited screen real-estate for displaying the reviews. Previous approaches have addressed the problem by ranking reviews according to their (estimated) helpfulness. However, such approaches do not account for the fact that the top few high-quality reviews may be highly redundant, repeating the same information, or presenting the same positive (or negative) perspective. In this work, we focus on the problem of selecting a comprehensive set of few high-quality reviews that cover many different aspects of the reviewed item. We formulate the problem as a maximum coverage problem, and we present a generic formalism that can model the different variants of review-set selection. We describe algorithms for the different variants we consider, and, whenever possible, we provide approximation guarantees with respect to the optimal solution. We also perform an experimental evaluation on real data in order to understand the value of coverage for users. <s> BIB036 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> As online reviews increasingly become part of the purchasing process, it is important to understand which components of these reviews consumers consider most helpful in facilitating the purchase decision process. Online retailer and rating websites with more helpful reviews offer greater potential value to their consumers. Through two studies, we seek to identify and better understand what makes a helpful consumer review. After an open-ended analysis of the qualities of a review identified by subjects' as helpful, we conducted a controlled experiment that manipulated both the review content and the description of the reviewer. One key finding is that reviews written by a self-described expert are more helpful than those that are not. This information can provide guidance to online retailers and rating websites in their efforts to provide value to their customers. <s> BIB037 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> As numerous on-line product reviews that vary in quality are published every day, much attention is being paid to quality assessment of such reviews. The current metric of using the number of votes by other customers such as 'helpful vote', despite its dominance, does not yield a fully effective outcome. In this article, we propose a novel metric to rank product reviews by 'mentions about experiences', accounting for customer's personal experiences, as a way of identifying high quality reviews. The proposed metric has two parameters that capture time expressions related to the use of products and product entities over different purchasing time periods by linguistic clues. The empirical results show that this metric is not only as helpful as the best existing metrics, 'helpful vote' or 'reviewer rank', but is also free from undesirable biases that either penalize recency or are driven solely by popularity. Our usability study also shows that ordering reviews by our metric is considered helpful on the accounts of both usefulness and satisfaction. <s> BIB038 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a user's evaluation of the review quality. <s> BIB039 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review text's stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores. <s> BIB040 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Large amounts of online reviews, the valuable voice of the customer, benefit consumers and product designers. Identifying and analyzing helpful reviews efficiently and accurately to satisfy both current and potential customers' needs have become a critical challenge for market-driven product design. Existing evaluation methods only use the review voting ratios given by customers to measure helpfulness. Due to the issues such as viewpoints of interest, technical proficiency and domain knowledge involved, it may mislead designers in identifying those truly valuable and insightful opinions from designers' perspective. Thus, in this study, we initiate our work to explore a possible approach that bridges the opinions expressed by consumers and the understanding gathered by designers in terms of how helpful these opinions are. Our ultimate research focus is on how to automatically evaluate the helpfulness of an online review from a designer's viewpoint entirely based on the review content itself. We start our work by first conducting an exploratory study to understand the fundamental question of what makes an online customer review helpful from product designers' viewpoint. Through our study, we propose four categories of features that reflect designers' concerns in judging review helpfulness. Based on our experiments, it reveals that discrepancy does exist between both online customer voting and designers' rating. Furthermore, for the cases where review ratings are not steadily available, we have proposed to use regression to predict and interpret review helpfulness with the help of the aforementioned four categories of features that are entirely extracted from review content. Finally, using review data crawled from Amazon.com and real ratings given by design personnel, it demonstrates the effectiveness of our proposal and it also suggests that helpful product reviews can be identified from a designer's angle by automatically analyzing the review content. We argue that the study reported is able to improve designer's ability in business intelligence processing by offering more targeted customer opinions. It highlights the urgency to uncover sensible user requirements from such quality opinions in our future research. <s> BIB041 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> For consumers, online product reviews have become an important source for product-related information. Furthermore, they represent a beneficial addition to online retailers' websites. Due to the increasing amount of available product reviews, identifying the most helpful product reviews represents an im- portant task in order to reduce information overload. Therefore, the factors in- fluencing review helpfulness have to be identified. Thus, in order to explain re- view helpfulness, we build upon and extend review diagnosticity theory with concepts from marketing research and propose a research model that includes product quality, review sentiment and review uncertainty. Based on a sample of amazon.com product reviews, we evaluate our research model and find that statements about product quality positively influence review helpfulness. Fur- thermore, we identify that sentiment as well as uncertainty expressed in product reviews have an impact on review helpfulness. Finally, we confirm that the product category has a moderating effect on these relationships. <s> BIB042 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> The paper addresses an opinion mining problem: how to find the helpful reviews from online consumer reviews via the quality of the content. Since there are too many reviews, efficiently identifying the helpful ones earlier can benefit both consumers and companies. Consumers can read only the helpful opinions from helpful reviews before they purchase a product, while companies can acquire the true reasons a product is liked or hated. A system is built to assess the difficulty of the problem. The experimental results show that helpful reviews can be distinguished from unhelpful ones with high precision. <s> BIB043 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> With the great development of e-commerce, users can create and publish a wealth of product information through electronic communities. It is difficult, however, for manufacturers to discover the best reviews and to determine the true underlying quality of a product due to the sheer volume of reviews available for a single product. The goal of this paper is to develop models for predicting the helpfulness of reviews, providing a tool that finds the most helpful reviews of a given product. This study intends to propose HPNN (a helpfulness prediction model using a neural network), which uses a back-propagation multilayer perceptron neural network (BPN) model to predict the level of review helpfulness using the determinants of product data, the review characteristics, and the textual characteristics of reviews. The prediction accuracy of HPNN was better than that of a linear regression analysis in terms of the mean-squared error. HPNN can suggest better determinants which have a greater effect on the degree of helpfulness. The results of this study will identify helpful online reviews and will effectively assist in the design of review sites. <s> BIB044 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> While online product reviews are valuable sources of information to facilitate consumers’ purchase decisions, it is deemed meaningful and important to distinguish helpful reviews from unhelpful ones for consumers facing huge amounts of reviews nowadays. Thus, in light of review classification, this paper proposes a novel approach to identifying review helpfulness. In doing so, a Bayesian inference is introduced to estimate the probabilities of the reviews belonging to respective classes, which differs from the traditional approach that only assigns class labels in a binary manner. Furthermore, an extended fuzzy associative classifier, namely GARCfp, is developed to train review helpfulness classification models based on review class probabilities and fuzzily partitioned review feature values. Finally, data experiments conducted on the reviews from amazon.com reveal the effectiveness of the proposed approach. <s> BIB045 </s> A Survey of Quality Prediction of Product Reviews <s> IV. CLASSIFICATION AND OTHER METHODS <s> Presents novel linguistic features for predicting helpfulness of online reviews.Describes a method to automatically extract linguistic features from review text.Builds a predictive model and empirically evaluates it on amazon review datasets.The model is found to be a better predictor of helpfulness for experience goods. Online reviews play a critical role in customer's purchase decision making process on the web. The reviews are often ranked based on user helpfulness votes to minimize the review information overload problem. This paper examines the factors that contribute towards helpfulness of online reviews and builds a predictive model. The proposed predictive model extracts novel linguistic category features by analysing the textual content of reviews. In addition, the model makes use of review metadata, subjectivity and readability related features for helpfulness prediction. Our experimental analysis on two real-life review datasets reveals that a hybrid set of features deliver the best predictive accuracy. We also show that the proposed linguistic category features are better predictors of review helpfulness for experience goods such as books, music, and video games. The findings of this study can provide new insights to e-commerce retailers for better organization and ranking of online reviews and help customers in making better product choices. <s> BIB046
In addition to ranking reviews according to their quality and utility, researchers also used classification methods to determine the quality of reviews. In most previous studies, helpfulness votes were used as the ground-truth data for training and testing regression models BIB007 BIB011 . However, in a different approach, these approaches were unreliable because of the previously mentioned types of biases discovered from their extensive analysis BIB012 . Thus, they did not use userhelpfulness feedback (helpfulness votes) as the ground-truth in training and testing their model. Their work focused on improving the quality of opinion summarization by detecting and discarding noisy and low quality reviews using a classification based approach. The proposed approach explores three features of product reviews: readability, informativeness and subjectivity. A set of specifications was proposed for judging the quality of reviews, and four categories defined: "best reviews", "good reviews", "fair reviews" and "bad reviews". A SVM was used to perform binary classification, with the "bad review" category as the low quality class and the remaining categories as the high quality class. After the classification step, only high quality reviews were used in generating opinion summarization. O'Mahony and Smyth BIB021 proposed a classification-based recommender system to recommend the most helpful reviews to the end user. Many features were used to train a classifier to distinguish between helpful and non-helpful reviews: reputation, content, social and sentiment features. The reputation and sentiment features achieved better classification performance than content and social features. A significant finding was that the classification performance remained high, even in the absence of the reputation features, which are not always available. Chien and Tseng BIB034 treated the quality problem as a classification problem by employing a multiclass SVM model to classify product reviews. In order to derive informative review features, an information quality "IQ" framework adopted from BIB002 was employed. Table 1 illustrates the information quality categories along with their dimensions. The authors defined five classes of quality: high, medium, low, duplicated and spam, and the specifications of review quality were adopted from BIB012 . Furthermore, factors that shape high quality reviews were analyzed and the findings show that helpful reviews need to be subjective and provide detailed comments on a number of product aspects. In a recent study, Bayesian inference was used to measure the probabilities of the reviews belonging to certain classes BIB045 . In addition, an extended fuzzy associative classifier was developed to train a review helpfulness classification model. The model incorporated features from previous studies BIB012 BIB034 BIB038  BIB035 BIB046 , for example, subjectivity features, emotion features and stylistic features. In a different approach from the previous supervised methods, Tsur and Rappoport [23] introduced a fully unsupervised method to rank book reviews according to review helpfulness. First, the proposed REVRANK algorithm identifies the dominant terms in a set of review documents. These important terms represent a "virtual optimal" or a core review representation. Subsequently, reviews are mapped to this optimal representation and a ranking score is given to each review according to distance between the review and the virtual review. All reviews of a given book were explored to generate a lexicon of the dominant concepts. This is relevant to keyphrase extraction proposed in the TextRank and the CollabRank systems BIB004 BIB015 . Both systems employ a graph-based unsupervised ranking algorithm which ranks keyphrases, using the co-occurrence links between words in the TextRank system and the collaborative knowledge from multiple documents in the CollabRank system. In recent work, a new problem of personalized review quality prediction was addressed to recommend helpful reviews BIB039 . The authors argue that the quality of reviews may www.ijacsa.thesai.org not be the same for different readers, while all the previous studies assume that it is. They found that there are some latent features that affect the user's evaluation of the quality of the review. Based on this assumption, a series of probabilistic graphical models, based on matrix factorization and tensor factorization, were proposed. The experiment was conducted on a real-life dataset from Eopinion.com, and the results show that the proposed technique outperformed the existing state-ofthe-art approaches, at that time, using textual and social features. All the above studies did not consider that highly ranked reviews may include highly redundant information  . Another method was proposed to solve this problem by selecting a small comprehensive set of high quality reviews BIB036 . These reviews cover many different aspects and viewpoints of the reviewed product. The authors of BIB036 extended existing algorithms for maximizing coverage to handle this problem. Their work is different in that they selected a set of comprehensive reviews rather than scoring each review. Furthermore, the proposed approach is different from opinion summarization because it aims to identify a subset of reviews that cover the different aspects of a product rather than summarizing the opinions on the extracted features of a product. The most related work to BIB036 is the work of Lappas and Gunopulos BIB029 . However, whereas the goal of the former authors is to cover the product aspect from a fixed size set with both negative and positive opinions, that of the latter is to cover all product aspects while preserving opinion distribution. Miao, Li, and Dai BIB022 , introduced a sentiment mining and retrieval system which is concerned with mining useful information from customers' reviews. They employed both data mining and information retrieval techniques to build a novel temporal opinion quality and relevance ranking system, which mines customers' preferences. Wu, Greene, and Cunningham BIB030 compared two aggregation methods for combining sets of features in order to identify untruthful opinions about hotels. Their solution was to build a useful suspicious-review ranking system. The results show that the best features to identify suspicious reviews are: proportion of positive singleton reviews, truncated rating, and reactive positive singleton reviews. Furthermore, it was found that singular value decomposition outperforms the unsupervised hedge algorithm for combining features to identify suspicious reviews about hotels. Although this work falls under spam detection, it is related to identifying qualitative reviews. Determining criteria for identifying suspicious reviews would improve the identification of reliable and trustworthy reviews. Lau, Zhang, Xia, and Song BIB031 proposed a method to detect non-informative online opinionated expressions. The proposed multi-facet quality metric utilizes both the intrinsic properties of opinionated expressions and association with other opinionated expressions posted on the Internet. Furthermore, to avoid the biases mentioned in BIB012 , helpfulness votes were not used as the ground-truth for quality assessment. A novel approach to assess the quality of product reviews was proposed by Min and Park BIB038 . The proposed metric employs linguistic clues to capture time expressions related to the use of the product and product aspects during different purchase times. They found that tense and time expressions are the most useful linguistic clues to assess the customer's previous purchase experiences. This approach is similar to the work done by the group in BIB032 , however there is a difference, because this work uses the reviewer's social context information based on the social network-based review website, such as the PageRank score of the author. Features used in this work improve system performance, however, they have limitations similar to the "helpfulness votes". In contrast, the method proposed by Min and Park extracts the reviewer's characteristics directly from the textual content of a review by utilizing his/her experience. The authors in BIB041 proposed a method to evaluate the helpfulness of online reviews based on the domain user's perspective, such as manufacturing engineers and product designers. They conducted an exploratory study to understand what makes reviews useful to designers. Four categories of features were proposed, based on their experiment, to identify helpful reviews: product features, linguistic features, features using information theory and features based on information quality. Machine learning algorithms were employed using both classification and regression to evaluate the proposed method. The results show a strong correlation between the designer"s rating and the proposed method. Another classification approach to modeling the helpfulness problem was proposed by Zeng and Wu BIB043 . A three-class classification framework was introduced to find the helpful positive reviews, the helpful negative reviews and to filter out the unhelpful reviews. Table 2 lists the features used in the classification approach. Some of the features were adopted from BIB007 and other features were added based on the findings of BIB037 . The study uses the list of common ideas related to helpfulness and unhelpfulness proposed in BIB037 . The performance of the three-class problem is quite high and the results show that helpful reviews (positive and negative) can be identified with high precision from unhelpful ones BIB037 . Recently, the work of Krishnamoorthy BIB046 has developed a new method for extracting linguistic features based on the linguistic category model (LCM), proposed by Semin and Fiedler BIB001 . A binary classifier was built and evaluated using the LCM with a combination of other features, namely: metadata features (e.g., review extremity), readability features, and subjectivity features (i.e., the total number of subjective words normalized by review length). The experiment on two real-life review datasets shows that linguistic category features are better predictors for the helpfulness of product reviews. Table 3 presents the linguistic category features used in the LCM. V. RELATED TASKS Researchers from many fields have shown great enthusiasm in studying and analyzing the quality of online reviews BIB012 BIB007 BIB008 BIB011 BIB016 BIB032 BIB023 . Pang and Lee BIB005 carried out the first study related to this problem. They studied the prediction of product rating, which correlates with the perceived helpfulness of reviews BIB007 . Automatically scoring essays is another related study which has been used to rate the quality of reviews BIB009 . The work group of BIB007 built a regression model to rank reviews according to their quality, employing the same set of features used in essay scoring by BIB009 . By using statistical methods, Jindal and Liu  discovered that the extracted features from reviews, such as rating and title length, improved the identification of spammed and duplicated reviews. The task of evaluating the quality of Web posts and the quality of answers in question and answering systems is also related to predicting the helpfulness of reviews BIB013 BIB014 BIB010 BIB017 BIB018 BIB024 . A study by Hoe, Li, and Zou BIB025 was to determine whether a review would achieve helpfulness votes, or not, through examining the posting time and textual features of a review. The work in BIB018 integrates user and community features with the review textual features to assess the quality of questions and answers using classification methods. The model utilizes community features which were driven from the answer text, for example the length of the answer and number of points received, in addition to the user"s features, such as number of answers given. The authors of BIB024 introduce a cotraining method to model the quality of both the review and the reviewer in an attempt to use community information for extracting features. The identification of high quality reviews influences feedback and reputation systems. It was found that seller and buyer behaviour changes in response to the change in a seller's feedback profile BIB006 . In another similar study BIB003 , it was found that personal reputation positively influences buyers to purchase a product, thus a small amount of negative feedback will not affect sales. However, most research on the quality of reviews analyzes the personal reputation impact on the review itself, rather than on the seller"s/buyer's transactions BIB026 . In addition, the work in BIB019 has also examined the impact of high quality reviews on purchase decisions and thus on sales. The results show that reviews with a high proportion of helpful votes are more important in making purchase decisions than the aggregate star rating of a product. Another important finding is that the reviewer"s reputation has no impact on the consumer purchase decision, which contradicts the findings of BIB026 . The work in BIB033 proposed a regression model for predicting sales performance using product reviews. This was one of the first attempts to examine the economic impacts of review quality. The authors of BIB027 studied the problem of product review search. The retrieved reviews are ranked according to their quality based on a given query. Other studies tackle the problem of finding good answers from questionanswering systems such as Yahoo! Answers BIB020 . A method of determining the quality of the answer was proposed by BIB028 , which considered both textual features from the answers and social features from the answerer. Some studies are interested in determining the fundamental characteristics of helpful reviews from a theoretical and practical point of view  BIB040 BIB035 BIB042 BIB044 . For example, Mudambi and Schuff examined factors contributing to review helpfulness, by employing a linear regression model. The study shows that product type, review extremity and review depth affect review helpfulness  . They analyzed the different impact of product type (search or experience goods) on review helpfulness in the multistage consumer decision process. Search goods are defined as products about which consumers can easily obtain information about their quality before purchasing. Experience goods are products that require purchasing in order to evaluate their quality, for example, books and movies. Thus, the type of product affects a consumer"s textual evaluation of a product and consequently affects the perceived helpfulness of that review. The study in BIB035 examines what factors affect the number of helpfulness votes reviews receive. It is argued that knowing the most important factors to attract helpfulness votes will help website designers to improve their helpfulness voting mechanism. For example, the findings show that semantic features have the most impact on the number of helpfulness votes. The conclusion is that websites should provide more ranking options to rank reviews rather than ranking the most recent review. Siering and Muntermann BIB042 confirm that product type influences the perceived helpfulness as was discovered in  . Furthermore, they find that perceived helpfulness is affected by other textual aspects related to review sentiment, product quality and review uncertainty. Lee and Choeh BIB044 1 http://answer.yahoo.com/