A concept-level approach to the analysis of online review helpfulness

Helpfulness of online reviews serves multiple needs of different Web users. Several types of factors can drive reviews' helpfulness. This study focuses on uninvestigated factors by looking at not just the quantitative factors (such as the number of concepts), but also qualitative aspects of reviewers (including review types such as the regular, comparative and suggestive reviews and reviewer helpfulness) and builds a conceptual model for helpfulness prediction. The set of 1500 reviews were randomly collected from TripAdvisor.com across multiple hotels for analysis. A set of four hypotheses were used to test the proposed model. Our results suggest that the number of concepts contained in a review, the average number of concepts per sentence, and the review type contribute to the perceived helpfulness of online reviews. The regular reviews were not statistically significant predictors of helpfulness. As a result, review types and concepts have a varying degree of impact on review helpfulness. The findings of this study can provide new insights to e-commerce retailers in understanding the importance of helpfulness of reviews. We have examined the factors that participate in online reviews helpfulness.The regular, comparative and suggestive are included as qualitative aspect.The numbers of concepts in opinion types are examined as quantitative factors.Further challenges are discussed for coming researchers.

[1]  Christopher S. G. Khoo,et al.  Aspect-based sentiment analysis of movie reviews on discussion boards , 2010, J. Inf. Sci..

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

[3]  R. G. Raj,et al.  A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective , 2014, TheScientificWorldJournal.

[4]  Judit Bar-Ilan,et al.  Information quality assessment of community generated content: A user study of Wikipedia , 2011, J. Inf. Sci..

[5]  Erik Cambria,et al.  SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis , 2014, AAAI.

[6]  Stephen Shaoyi Liao,et al.  Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses , 2011, Int. J. Intell. Inf. Technol..

[7]  Bryan A. Garner.  Legal Writing in Plain English: A Text with Exercises , 2001 .

[8]  Dipankar Das,et al.  Enriching SenticNet Polarity Scores through Semi-Supervised Fuzzy Clustering , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[9]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[10]  Shanmugasundaram Hariharan,et al.  Opinion mining and summarization of reviews in web forums , 2010, Bangalore Compute Conf..

[11]  William Hart-Davidson,et al.  A method for measuring helpfulness in online peer review , 2010, SIGDOC '10.

[12]  Ofer Arazy,et al.  On the measurability of information quality , 2011, J. Assoc. Inf. Sci. Technol..

[13]  Erik Cambria,et al.  Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis , 2015 .

[14]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

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

[16]  Bing Liu,et al.  Identifying comparative sentences in text documents , 2006, SIGIR.

[17]  John W. Payne,et al.  Effort and Accuracy in Choice , 1985 .

[18]  Anindya Ghose,et al.  Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets , 2008, Inf. Syst. Res..

[19]  Fermín L. Cruz,et al.  A knowledge-rich approach to feature-based opinion extraction from product reviews , 2010, SMUC '10.

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

[21]  Sally Mckechnie.  Consumer Buying Behaviour in Financial Services: An Overview , 1992 .

[22]  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 .

[23]  Paolo Gastaldo,et al.  An ELM-based model for affective analogical reasoning , 2015, Neurocomputing.

[24]  Chris Ryan,et al.  Perceptions of place, modernity and the impacts of tourism – Differences among rural and urban residents of Ankang, China: A likelihood ratio analysis , 2011 .

[25]  Ritesh Kumar.  Making Machine Translations Polite: The Problematic Speech Acts , 2011, ICIS 2011.

[26]  Erik Cambria,et al.  AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis , 2015, AAAI.

[27]  Erik Cambria,et al.  Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns , 2015, IEEE Computational Intelligence Magazine.

[28]  Donna F. Davis,et al.  Examining Market Information and Brand Equity Through Resource-Advantage Theory: A Carrier Perspective , 2012 .

[29]  Stephen Shaoyi Liao,et al.  Mining comparative opinions from customer reviews for Competitive Intelligence , 2011, Decis. Support Syst..

[30]  Peter E. Kennedy.  A Guide to Econometrics , 1979 .

[31]  Daniel Neagu,et al.  Assessing information quality of e-learning systems: a web mining approach , 2011, Comput. Hum. Behav..

[32]  Marian Quigley.  Encyclopedia of Information Ethics and Security , 2007 .

[33]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[34]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[35]  Bing Liu,et al.  Mining Opinions in Comparative Sentences , 2008, COLING.

[36]  Diego Reforgiato Recupero,et al.  Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool , 2014, IEEE Computational Intelligence Magazine.

[37]  Davide Anguita,et al.  Can machine learning explain human learning? , 2016, Neurocomputing.

[38]  Chuan-Hoo Tan,et al.  Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features , 2013, Int. J. Electron. Commer..

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

[40]  Rajiv Kohli,et al.  Understanding Determinants of Online Consumer Satisfaction: A Decision Process Perspective , 2004, J. Manag. Inf. Syst..

[41]  Bing Liu,et al.  Mining Comparative Sentences and Relations , 2006, AAAI.

[42]  Zhoujun Li,et al.  Comparable Entity Mining from Comparative Questions , 2010, ACL.

[43]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

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

[45]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

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

[47]  Erik Cambria,et al.  Merging SenticNet and WordNet-Affect emotion lists for sentiment analysis , 2012, 2012 IEEE 11th International Conference on Signal Processing.

[48]  Guo-Hui Li,et al.  Mining Chinese comparative sentences by semantic role labeling , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[49]  Richong Zhang,et al.  A Novel Approach for Recommending Ranked User-Generated Reviews , 2010, Canadian Conference on AI.

[50]  J. Crotts,et al.  The perceived usefulness of blog postings: An extension of the expectancy-disconfirmation paradigm , 2011 .

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

[52]  Maged Ali,et al.  Perceived derived attributes of online customer reviews , 2016, Comput. Hum. Behav..

[53]  B. Sparks,et al.  Online travel reviews as persuasive communication: The effects of content type, source, and certification logos on consumer behavior , 2013 .

[54]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

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

[56]  Erik Cambria,et al.  Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article] , 2014, IEEE Computational Intelligence Magazine.

[57]  Erik Cambria,et al.  Enhancing Business Intelligence by Means of Suggestive Reviews , 2014, TheScientificWorldJournal.