Cristian Danescu-Niculescu-Mizil
Jon Kleinberg
Lillian Lee
ABSTRACT
There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from ifferent countries.
- E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. Finding high-quality content in social media. In procof WSDM, 2008. Google Scholar
Digital Library
- A. Agresti. phAn Introduction to Categorial Data Analysis. Wiley, 1996.Google Scholar
- T. Amabile. Brilliant but cruel: Perception of negative evaluators. Journal of Experimental Social Psychology, 19: 146--156, 1983.Google ScholarCross Ref
- {4}R. Bond and P. B. Smith. Culture and conformity: A meta-analysis of studies using Asch's (1952b, 1956) line judgment task. Psychological Bulletin, 119 (1): 111--137, 1996.Google ScholarCross Ref
- J. A. Chevalier and D. Mayzlin. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43 (3): 345--354, August 2006.Google ScholarCross Ref
- R. B. Cialdini and N. J. Goldstein. Social influence: compliance and conformity. Annual Review of Psychology, 55: 591--621, 2004.Google ScholarCross Ref
- S. David and T. J. Pinch. Six degrees of reputation: The use and abuse of online review and recommendation systems. First Monday, July 2006. Special Issue on Commercial Applications of the Internet.Google ScholarCross Ref
- J. Escalas and J. R. Bettman. Self-construal, reference groups, and brand meaning. Journal of Consumer Research, 32 (3): 378--389, 2005.Google ScholarCross Ref
- G. J. Fitzsimons, J. W. Hutchinson, P. Williams, and J. W. Alba. Non-conscious influences on consumer choice. Marketing Letters, 13 (3): 267--277, 2002.Google ScholarCross Ref
- A. Ghose and P. G. Ipeirotis. Designing novel review ranking systems: Predicting usefulness and impact of reviews. In procofthe Intl. Conf. on Electronic Commerce (ICEC), 2007. Invited paper. Google ScholarDigital Library
- A. Ghose and P. G. Ipeirotis. Estimating the socio-economic impact of product reviews: Mining text and reviewer characteristics. SSRN working paper, 2008. Available at http://ssrn.com/paper=1261751. Version dated September 1.Google Scholar
- F. M. Harper, D. Raban, S. Rafaeli, and J. A. Konstan. Predictors of answer quality in online Q&A sites. In procof CHI, 2008. Google ScholarDigital Library
- N. Hu, P. A. Pavlou, and J. Zhang. Can online reviews reveal a product's true quality? Empirical findings and analytical modeling of online word-of-mouth communication. In procof Electronic Commerce (EC), pages 324--330, 2006. Google ScholarDigital Library
- N. Jindal and B. Liu. Review spam detection. In procof WWW, 2007. Poster paper. Google ScholarDigital Library
- S.-M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In procof EMNLP, pages 423--430, July 2006. Google ScholarDigital Library
- B. Knutson, S. Rick, G. E. Wimmer, D. Prelec, and G. Loewenstein. Neural predictors of purchases. Neuron 53: 147--156, 2007.Google ScholarCross Ref
- J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion summarization. In procof EMNLP-CoNLL, pages 334--342, 2007. Poster paper.Google Scholar
- B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2): 1--135, 2008. Google ScholarDigital Library
- S. Sen, F. M. Harper, A. LaPitz, and J. Riedl. The quest for quality tags. In procof GROUP, 2007. Google ScholarDigital Library
- D. Sorokina, J. Gehrke, S. Warner, and P. Ginsparg. Plagiarism detection in arXiv. In procof ICDM, pages 1070--1075, 2006. Google ScholarDigital Library
- P. Underhill. Why We Buy: The Science Of Shopping. Simon & Schuster, New York, 1999. Google ScholarDigital Library
- H. T. Welser, E. Gleave, D. Fisher, and M. Smith. Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 2007.Google Scholar
- F. Wu and B. A. Huberman. How public opinion forms. In Proc. of the Wksp. on Internet and Network Economics (WINE), 2008. Short paper. Google ScholarDigital Library
- Z. Zhang and B. Varadarajan. Utility scoring of product reviews. In Proc. of CIKM, pages 51--57, 2006. Google ScholarDigital Library
Index Terms
- How opinions are received by online communities: a case study on amazon.com helpfulness votes
Recommendations
Integrated microblog sentiment analysis from users' social interaction patterns and textual opinions
Traditional post-level opinion classification methods usually fail to capture a person's overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the ...
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Detecting General Opinions from Customer Surveys
ICDMW '11: Proceedings of the 2011 IEEE 11th International Conference on Data Mining WorkshopsQuestionnaire-based surveys and on-line product reviews resemble each other in that they both have user comments and satisfaction ratings. Since a comment might be a general opinion about a product or only one or a set of its attributes, in which case ...
Comments