A Survey of Quality Prediction of Product Reviews <s> II. QUALITY OF REVIEWS <s> User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating. <s> BIB001 </s> A Survey of Quality Prediction of Product Reviews <s> II. QUALITY OF REVIEWS <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> BIB002 </s> A Survey of Quality Prediction of Product Reviews <s> II. QUALITY OF REVIEWS <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> BIB003
The topic of quality of reviews is related to opinion spam detection, which makes it an important area of research. However, according to Liu  , it is different from spam detection, as spam reviews may not be of low quality. Fake reviews may be of high quality, especially if they are well written, which makes them hard to identify. Determining good quality reviews saves readers time and effort by discarding noisy and low quality content. It is useful to have a mechanism to automatically assess a review"s helpfulness as soon as it is written. Some aggregation and hosting websites rank reviews according to their perceived helpfulness by readers, such as Amazon.com, Epinions, IMDB CitySearch, etc. Users manually assess reviews by responding to a question, such as "Was this review helpful to you?". Readers can respond with "YES or NO" and the feedback results are calculated and displayed next to each review (e.g., "12 of 20 people found the following review helpful"). Although this helpfulness evaluation method has been used by many websites, it is still a meaningful task to automatically determine the quality of each review for the following reasons: www.ijacsa.thesai.org reviews lack a helpfulness evaluation: 38% of 20,000 Amazon MP3-player reviews only received three or less votes in three months BIB001 . In addition, consumers are not obligated to respond to the feedback question to determine a review's perceived helpfulness, even if they found it helpful or not. 2) Human generated helpfulness evaluation may be fake, which makes the helpfulness voting score untrustworthy BIB003 . Spammers can click on the helpfulness voting buttons (Yes or No) to increase or decrease the helpfulness of a review. Therefore, depending on the helpfulness feedback to identify helpful reviews can be problematic. 3) Biases can be found in the manual helpfulness evaluations BIB002 . Reviews with high helpfulness score are prominently displayed, which would have a disproportionate influence on readers and consequently on the helpfulness voting score itself. This type of bias is referred to as "winner circle" bias in BIB002 . In addition, an in-depth analysis of Amazon's highly-voted reviews, lead to discover that some of the reviews are not of as good quality as the helpfulness voting score indicates. Readers tend to value others' reviews positively, which makes the distribution of helpfulness evaluation skewed towards the helpful vote, known as the "imbalance vote bias". The third type of bias identified is called "early bird bias" BIB002 . The helpfulness voting score may take a long time to accumulate, particularly in newly posted reviews. Earlier posted reviews are displayed to readers for a longer time than newly posted reviews. 4) The use of robust review quality prediction systems will facilitate ranking reviews according to their utility, and thus users can easily and quickly access them. Furthermore, applications such as sentiment extraction and opinion summarization will benefit from such systems by operating on high quality content rather than spammed and misleading reviews. For example, in the process of opinion summarization it is useful to only use good quality reviews and discard low quality ones, including reviews with high helpfulness voting score, which are subject to the previously mentioned biases. Therefore, automatically classifying reviews according to their quality would aid and speed up the quality of opinion summarization BIB002 .