A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <s> Quality uncertainty and high search costs for identifying relevant information from an ocean of information may prevent customers from making purchases. Recognizing potential negative impacts of this search cost for quality information and relevant information, firms began to invest in creating a virtual community that enables consumers to share their opinions and experiences to reduce quality uncertainty, and in developing recommendation systems that help customers identify goods in which they might have an interest. However, not much is known regarding the effectiveness of these efforts. In this paper, we empirically investigate the impacts of recommendations and consumer feedbacks on sales based on data gathered from Amazon.com. Our results indicate that more recommendations indeed improve sales at Amazon.com; however, consumer ratings are not found to be related to sales. On the other hand, number of consumer reviews is positively associated with sales. We also find that recommendations work better for less-popular books than for more-popular books. This is consistent with the search cost argument: a consumer’s search cost for less-popular books may be higher, and thus they may rely more on recommendations to locate a product of interest. <s> BIB001 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <s> We present a prototype system, code-named Pulse, for mining topics and sentiment orientation jointly from free text customer feedback. We describe the application of the prototype system to a database of car reviews. Pulse enables the exploration of large quantities of customer free text. The user can examine customer opinion “at a glance” or explore the data at a finer level of detail. We describe a simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface. <s> BIB002 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <s> The creation of online consumer communities to provide product reviews and advice has been touted as an important, albeit somewhat expensive component of Internet retail strategies. In this paper, we characterize reviewer behavior at two popular Internet sites and examine the effect of consumer reviews on firms' sales. We use publicly available data from the two leading online booksellers, Amazon.com and BarnesandNoble.com, to construct measures of each firm's sales of individual books. We also gather extensive consumer review data at the two sites. First, we characterize the reviewer behavior on the two sites such as the distribution of the number of ratings and the valence and length of ratings, as well as ratings across different subject categories. Second, we measure the effect of individual reviews on the relative shares of books across the two sites. We argue that our methodology of comparing the sales and reviews of a given book across Internet retailers allows us to improve on the existing literature by better capturing a causal relationship between word of mouth (reviews) and sales since we are able to difference out factors that affect the sales and word of mouth of both retailers, such as the book's quality. We examine the incremental sales effects of having reviews for a particular book versus not having reviews and also the differential sales effects of positive and negative reviews. Our large database of books also allows us to control for other important confounding factors such as differences across the sites in prices and shipping times. <s> BIB003 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <s> Abstract This article uses actual word-of-mouth (WOM) information to examine the dynamic patterns of WOM and how it helps explain box office revenue. The WOM data were collected from the Yahoo Movies Web site. The results show that WOM activities are the most active during a movie's prerelease and opening week and that movie audiences tend to hold relatively high expectations before release but become more critical in the opening week. More important, WOM information offers significant explanatory power for both aggregate and weekly box office revenue, especially in the early weeks after a movie opens. However, most of this explanatory power comes from the volume of WOM and not from its valence, as measured by the percentages of positive and negative messages. <s> BIB004 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <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> BIB005 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <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> BIB006 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <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> BIB007 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <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> BIB008 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <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> BIB009 </s> A Survey of Quality Prediction of Product Reviews <s> INTRODUCTION <s> As a new type of word-of-mouth information, online consumer product review is an emerging market phenomenon that is playing an increasingly important role in consumers' purchase decisions. This paper argues that online consumer review, a type of product information created by users based on personal usage experience, can serve as a new element in the marketing communications mix and work as free “sales assistants” to help consumers identify the products that best match their idiosyncratic usage conditions. ::: ::: This paper develops a normative model to address several important strategic issues related to consumer reviews. First, we show when and how the seller should adjust its own marketing communication strategy in response to consumer reviews. Our results reveal that if the review information is sufficiently informative, the two types of product information, i.e., the seller-created product attribute information and buyer-created review information, will interact with each other. For example, when the product cost is low and/or there are sufficient expert (more sophisticated) product users, the two types of information are complements, and the seller's best response is to increase the amount of product attribute information conveyed via its marketing communications after the reviews become available. However, when the product cost is high and there are sufficient novice (less sophisticated) product users, the two types of information are substitutes, and the seller's best response is to reduce the amount of product attribute information it offers, even if it is cost-free to provide such information. We also derive precise conditions under which the seller can increase its profit by adopting a proactive strategy, i.e., adjusting its marketing strategies even before consumer reviews become available. Second, we identify product/market conditions under which the seller benefits from facilitating such buyer-created information (e.g., by allowing consumers to post user-based product reviews on the seller's website). Finally, we illustrate the importance of the timing of the introduction of consumer reviews available as a strategic variable and show that delaying the availability of consumer reviews for a given product can be beneficial if the number of expert (more sophisticated) product users is relatively large and cost of the product is low. <s> BIB010
The Internet has made it possible to discover opinions of others on a wide range of subjects, through social media websites, such as review sites and wikis, and through online social networks. According to a survey, 81% of Internet users have done research on a product at least once BIB009 . Studies have found that customers" reviews can form others" opinions and subsequently affect sales  BIB001  . Understanding and analyzing public opinion is important for the prediction of future events. Consequently, this aids the process of making a decision that can involve improving services, handling political elections and calculating risk management. Organizations conduct consumer surveys to explore opinions about their products and/or services. However, the design and the supervision of these surveys are expensive, tedious and a time-consuming task BIB002 . It is easier for companies to utilize the freely available online consumer reviews. However, the explosive growth of opinion text on the Web makes it hard to manage. In addition, opinions posted on the Web in free-text style are less structured than those conducted from consumer surveys and focus groups, and require more effort to collect and analyze BIB010 BIB003 BIB004 . Sentiment analysis, also referred to as opinion mining, is a growing field in text mining technology, which is concerned with the analysis of people"s opinions, attitudes, evaluations and emotions, expressed in free-text fashion towards different objects, such as organizations, product attributes, public events and even individuals  . Sentiment analysis is motivated by the fact that individuals and organizations are increasingly using the content of social media for decision-making. Since 2000, there has been much attention paid to sentiment analysis research, mainly because of the rise of machine learning (ML) technology in natural language processing (NLP). In addition, the public datasets available for training using ML algorithms have aided sentiment analysis research. There have been major breakthroughs and promising results in research into sentiment analysis, especially in opinion summarization, feature extraction and polarity identification. However, the extensive amount of uncontrolled user-generated reviews on the Internet has raised concerns about their quality and reliability. There are many challenges in sentiment analysis research such as dealing with sarcasm and implicit opinions, domain dependency, subjectivity detection and entity identification. A significant challenge that has been studied is determining the quality, also called utility, helpfulness or usefulness, of product reviews BIB007 BIB005 BIB008 BIB006 . This survey will point out some of the significant research to handle the quality prediction problem. The rest of this paper is organized as follows. In section 2 we have introduced the challenge of "product reviews quality. Sections 3 and 4 give an overview of the methods used to predict the utility of product reviews. Section 5 discuses some of the research related to the quality prediction problem, while section 6 concludes the paper.