A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <s> Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for operations research/management science (OR/MS) research. <s> BIB001 </s> A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <s> Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files.We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global trust value, based on the peer's history of uploads. We present a distributed and secure method to compute global trust values, based on Power iteration. By having peers use these global trust values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network.In simulations, this reputation system, called EigenTrust, has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system. <s> BIB002 </s> A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <s> A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts/distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting. <s> BIB003 </s> A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <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> BIB004 </s> A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <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> BIB005 </s> A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <s> Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools. <s> BIB006 </s> A Survey of Quality Prediction of Product Reviews <s> VI. CONCLUSION <s> Product reviews and ratings are popular tools to support buying decisions of consumers. These tools are also valuable for online retailers, who use rating systems in order to build trust and reputation in the online market. Many online shops oer quantitative ratings, textual reviews or a combination of both. This paper examines the acceptance and usage of ratings and reviews in the context of e-commerce transactions. A survey among 104 German online shoppers was conducted to examine how consumer reviews and rat- ings are used to support buying decisions. The survey shows that reviews and ratings are an important source of information for consumers. However, qualitative feedback from the survey indicates that the perceived helpfulness of rating systems varies. Especially the comparison of user reviews is a very time consuming process for the customer, because of the unstructured nature of textual user reviews. In this paper we summarize similar problems and show corresponding examples to them. This will give new insight for future research in the area of user ratings and reviews. <s> BIB007
With e-commerce websites growing rapidly, reviews about products are becoming an important source of information for making informed purchase decisions. Sentiment analysis research is concerned with extracting and summarizing the massive content of product reviews. However, the explosive growth of product reviews raises concerns about their reliability and quality. Although review helpfulness is currently assessed manually by many retailer websites, it is important to automatically assess the quality of reviews at least for two reasons. The first reason is to provide helpfulness evaluation when human evaluations are lacking. The second reason is to correct skews in human helpfulness evaluations mentioned in BIB004 . Regression and classification methods have been examined to rank and classify reviews according to their helpfulness. Major challenges in quality prediction include feature weighting, which affects the performance of classifiers enormously. Another challenge is to consider the quality of the reviewer. Some trust metrics from other studies may be of use to determine the helpfulness of product reviews, for example, research into peer-to-peer and reputation networks BIB001 BIB003 BIB002   . Investigating what factors determine review helpfulness could improve review systems. Therefore, retailers" websites could introduce automatic helpfulness scoring systems to reduce customers" search cost. This would affect customers" satisfaction and purchase behaviour BIB005 BIB006 . Furthermore, there should be a method to increase the efficiency of reviews to support online purchase decisions. For example, introducing a mechanism to allow an easier comparison between reviews would affect the process of making a purchase decision BIB007 .