A Survey of Quality Prediction of Product Reviews

With the help of Web-2.0, the Internet offers a vast amount of reviews on many topics and in different domains. This has led to an explosive growth of product reviews and customer feedback, which presents the problem of how to handle the abundant volume of data. It is an expensive and time-consuming task to analyze this huge content of opinions. Therefore, the need for automated sentiment analysis systems is vital. However, these systems encounter many challenges; assessing the content quality of the posted opinions is an important area of study that is related to sentiment analysis. Currently, review helpfulness is assessed manually; however the task of automatically assessing it has gained more attention in recent years. This paper provides a survey of approaches to the challenge of identifying the content quality of product reviews.

[1]  Cyrill Gössi,et al.  Selecting a Comprehensive Set of Reviews , 2015 .

[2]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[3]  R. Schindler,et al.  Perceived Helpfulness of Online Consumer Reviews: The Role of Message Content and Style , 2010 .

[4]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[5]  Michael Scholz,et al.  The Recipe for the Perfect Review? , 2013, Business & Information Systems Engineering.

[6]  Panagiotis G. Ipeirotis,et al.  Designing novel review ranking systems: predicting the usefulness and impact of reviews , 2007, ICEC.

[7]  Ee-Peng Lim,et al.  Quality-aware collaborative question answering: methods and evaluation , 2009, WSDM '09.

[8]  W. Bruce Croft,et al.  A framework to predict the quality of answers with non-textual features , 2006, SIGIR.

[9]  Qing Cao,et al.  Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach , 2011, Decis. Support Syst..

[10]  P. Chatterjee,et al.  Online Reviews: Do Consumers Use Them? , 2006 .

[11]  Nikolaos Korfiatis,et al.  The Influences of Negativity and Review Quality on the Helpfulness of Online Reviews , 2011, ICIS.

[12]  Ann E. Schlosser.  Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments ☆ , 2011 .

[13]  Yue Pan,et al.  Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews , 2011 .

[14]  Xin Li,et al.  Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay , 2005, EC '05.

[15]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[16]  Xiaohui Yu,et al.  A quality-aware model for sales prediction using reviews , 2010, WWW '10.

[17]  Xiaohui Yu,et al.  Modeling and Predicting the Helpfulness of Online Reviews , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[18]  Max Mühlhäuser,et al.  Automatically Assessing the Post Quality in Online Discussions on Software , 2007, ACL.

[19]  Yubo Chen,et al.  Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix , 2004, Manag. Sci..

[20]  Gün R. Semin,et al.  The linguistic category model, its bases, applications and range , 1991 .

[21]  Chrysanthos Dellarocas,et al.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003, Manag. Sci..

[22]  Yuanyuan Hao,et al.  Why some online product reviews have no usefulness rating? , 2009, PACIS.

[23]  Roberto V. Zicari,et al.  Using Dependency Bigrams and Discourse Connectives for Predicting the Helpfulness of Online Reviews , 2014, EC-Web.

[24]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[25]  P. Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2006 .

[26]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[27]  Jill Burstein,et al.  AUTOMATED ESSAY SCORING WITH E‐RATER® V.2.0 , 2004 .

[28]  David C. Yen,et al.  A study of factors that contribute to online review helpfulness , 2015, Comput. Hum. Behav..

[29]  Paul Resnick,et al.  Reputation systems , 2000, CACM.

[30]  Xiaojun Wan,et al.  CollabRank: Towards a Collaborative Approach to Single-Document Keyphrase Extraction , 2008, COLING.

[31]  Zhilin Yang,et al.  Online service quality dimensions and their relationships with satisfaction: A content analysis of customer reviews of securities brokerage services , 2004 .

[32]  Martin Ester,et al.  ETF: extended tensor factorization model for personalizing prediction of review helpfulness , 2012, WSDM '12.

[33]  Thomas L. Ngo-Ye,et al.  The influence of reviewer engagement characteristics on online review helpfulness: A text regression model , 2014, Decis. Support Syst..

[34]  Georg Lackermair,et al.  Importance of Online Product Reviews from a Consumer's Perspective , 2013 .

[35]  Pearl Pu,et al.  Prediction of Helpful Reviews Using Emotions Extraction , 2014, AAAI.

[36]  Ming Zhou,et al.  Low-Quality Product Review Detection in Opinion Summarization , 2007, EMNLP.

[37]  David Schuff,et al.  Is It the Review or the Reviewer? a Multi-Method Approach to Determine the Antecedents of Online Review Helpfulness , 2011, 2011 44th Hawaii International Conference on System Sciences.

[38]  Barry Smyth,et al.  Learning to recommend helpful hotel reviews , 2009, RecSys '09.

[39]  Sangwon Park,et al.  What makes a useful online review? Implication for travel product websites. , 2015 .

[40]  Iryna Gurevych,et al.  Predicting the perceived quality of web forum posts , 2007 .

[41]  Mario Pandelaere,et al.  When Consistency Matters: The Effect of Valence Consistency on Review Helpfulness , 2015, J. Comput. Mediat. Commun..

[42]  马玥,et al.  Extending Associative Classifier to Detect Helpful Online Reviews with Uncertain Classes , 2015 .

[43]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[44]  Pradeep Racherla,et al.  Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories , 2012, Electron. Commer. Res. Appl..

[45]  Zhu Zhang,et al.  Utility scoring of product reviews , 2006, CIKM '06.

[46]  Jan Muntermann,et al.  AIS Electronic Library (AISeL) , 2000 .

[47]  Gilad Mishne,et al.  Finding high-quality content in social media , 2008, WSDM '08.

[48]  Elena García Barriocanal,et al.  Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content , 2012, Electron. Commer. Res. Appl..

[49]  Xiaoquan Zhang,et al.  AIS Electronic Library (AISeL) , 2017 .

[50]  Eric K. Ringger,et al.  Pulse: Mining Customer Opinions from Free Text , 2005, IDA.

[51]  Eugene Agichtein,et al.  Learning to recognize reliable users and content in social media with coupled mutual reinforcement , 2009, WWW '09.

[52]  Sangjae Lee,et al.  Predicting the helpfulness of online reviews using multilayer perceptron neural networks , 2014, Expert Syst. Appl..

[53]  Linh Hoang,et al.  A Model for Evaluating the Quality of User-Created Documents , 2008, AIRS.

[54]  Derek Greene,et al.  Merging multiple criteria to identify suspicious reviews , 2010, RecSys '10.

[55]  Ruwei Dai,et al.  AMAZING: A sentiment mining and retrieval system , 2009, Expert Syst. Appl..

[56]  Jahna Otterbacher,et al.  'Helpfulness' in online communities: a measure of message quality , 2009, CHI.

[57]  Ari Rappoport,et al.  RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews , 2009, ICWSM.

[58]  Jon M. Kleinberg,et al.  WWW 2009 MADRID! Track: Data Mining / Session: Opinions How Opinions are Received by Online Communities: A Case Study on Amazon.com Helpfulness Votes , 2022 .

[59]  Raymond Y. K. Lau,et al.  Multi-facets Quality Assessment of Online Opinionated Expressions , 2010, WISE Workshops.

[60]  Shen Huang,et al.  Discovering clues for review quality from author's behaviors on e-commerce sites , 2009, ICEC.

[61]  Han Zhang,et al.  Dreading and Ranting: The Distinct Effects of Anxiety and Anger in Online Seller Reviews , 2011, ICIS.

[62]  Jong C. Park,et al.  Identifying helpful reviews based on customer's mentions about experiences , 2012, Expert Syst. Appl..

[63]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[64]  Pei-Yu Sharon Chen,et al.  The Impact of Online Recommendations and Consumer Feedback on Sales , 2004, ICIS.

[65]  Hector Garcia-Molina,et al.  The Eigentrust algorithm for reputation management in P2P networks , 2003, WWW '03.

[66]  Chien Chin Chen,et al.  Quality evaluation of product reviews using an information quality framework , 2011, Decis. Support Syst..

[67]  Tsun Ku,et al.  Modeling the Helpful Opinion Mining of Online Consumer Reviews as a Classification Problem , 2013, ROCLING/IJCLCLP.

[68]  Srikumar Krishnamoorthy,et al.  Linguistic features for review helpfulness prediction , 2015, Expert Syst. Appl..

[69]  Michael D. Smith,et al.  All Reviews are Not Created Equal: The Disaggregate Impact of Reviews and Reviewers at Amazon.Com , 2008 .

[70]  Huan Liu,et al.  Context-aware review helpfulness rating prediction , 2013, RecSys.

[71]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[72]  Shen Huang,et al.  Improving product review search experiences on general search engines , 2009, ICEC.

[73]  Richard Y. K. Fung,et al.  Identifying helpful online reviews: A product designer's perspective , 2013, Comput. Aided Des..

[74]  Soo-Min Kim,et al.  Automatically Assessing Review Helpfulness , 2006, EMNLP.

[75]  Ramanathan V. Guha,et al.  Propagation of trust and distrust , 2004, WWW '04.

[76]  Yue Lu.  Exploiting Social Context for Review Quality Prediction , 2010 .

[77]  Dimitrios Gunopulos,et al.  Efficient Confident Search in Large Review Corpora , 2010, ECML/PKDD.

[78]  Zhewei Zhang,et al.  Why Aren't the Stars Aligned? An Analysis of Online Review Content and Star Ratings , 2014, 2014 47th Hawaii International Conference on System Sciences.