Classification of Informative Reviews Based on Personal Values
Yasufumi Takama, Zhongjie Mao, and Shunichi Hattori
Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
This paper proposes a method for classifying informative reviews based on personal values. Reviews of an item are useful for a user who is considering purchasing it. However, it is difficult for readers to find informative reviews from vast amount of reviews because of existence of too many uninformative reviews. This paper supposes that the value of a review is affected by reader-dependent and independent factors. Typical uninformative reviews in terms of reader-independent factor are copy-and-paste reviews, which do not provide any readers with useful information for their decision-making. On the other hand, it is supposed different readers regard different reviews as informative, which is affected by their personal values. This paper focuses on such a readerdependent factor, and proposes a methods for classifying informative reviews based on reader’s personal value. Experiments are conducted using actual review data provided by Rakuten Inc., of which the results show about 0.7 of average accuracy is achieved. Furthermore, it is also shown proposed method can model judging criteria common to those who have similar personal values.
-  K. Fujimoto, “An Investigation of Potency of eWOM Messages with a Focus on Subjective Rank Expressions,” WI-IAT2010, pp. 97-101, 2010.
-  K. Fujimoto, “A Computational Account of Potency Differences in eWOM Messages Involving Subjective Rank Expressions,” WIIAT2011, pp. 138-142, 2011.
-  S. Hattori and Y. Takama, “Investigation About Applicability of Personal Values for Recommender System,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.16, No.3, pp. 404-411, 2012.
-  S. Hattori, Z. Mao, and Y. Takama, “Proposal of User Modeling Method and Recommender System based on Personal Values,” SCIS&ISIS2012, F3-45-3, 2012.
-  S. Hattori and Y. Takama, “Proposal of User Modeling Method Employing Reputation Analysis on User Reviews Based on Personal Values,” JSAI2013 Int. Organized Session, 1A3-IOS-3a-4, 2013.
-  M. B. Holbrook, “Consumer Value: A Framework for Analysis and Research,” Routledge, 1999.
-  M. Rokeach, “The Nature of Human Values,” New York: The Free Press, 1973.
-  D. E. Vinson, J. E. Scott, and L. M. Lamont, “The Role of Personal Values in Marketing and Consumer Behavior,” The J. of Marketing, Vol.41, No.2, pp. 44-50, 1977.
-  Y. Kim and K. Shim, “TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling,” ICDM2011, pp. 340-349, 2011.
-  Z. Ren, J. Ma, G. Wang, C. Cui, and X. Han, “Dynamically Modeling Semantic Dependencies in Web Forum Threads,” WI-IAT2011, pp. 348-351, 2011.
-  P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R. Zadeh, “WTF: TheWho to Follow Service at Twitter,” WWW’13, pp. 505-514, 2013.
-  Q. Gao, F. Abel, G.-J. Houben, and K. Tao, “Interweaving Trend and User Modeling for Personalized News Recommendation,” WIIAT’ 11, pp. 100-103, 2011.
-  T. Hua, F. Chen, L. Zhao, C.-T. Lu, and N. Ramakrishnan, “STED: Semi-supervised Targeted-interest Event Detection in Twitter,” KDD’13, pp. 1466-1469, 2013.
-  A. Ritter, Mausam, O. Etzioni, and S. Clark, “Open Domain Event Extraction from Twitter,” KDD’12, pp. 1104-1112, 2012.
-  X. Meng, F. Wei, X. Liu, M. Zhou, S. Li, and H. Wang, “Entity-centric Topic-oriented Opinion Summarization in Twitter,” KDD’12, pp. 379-387, 2012.
-  L. Hong, A. Ahmed, S. Gurumurthy, A. Smola, and K. Tsioutsiouliklis, “Discovering Geographical Topics In The Twitter Stream,” WWW’12, pp. 769-778, 2012.
-  D. Gruhl, R. Guha, R. Kumar, J. Novak, and A. Tomkins, “The Predictive Power of Online Chatter,” KDD’05, pp. 78-87, 2005.
-  Y. Liu, X. Huang, A. An, and X. Yu, “ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs,” SIGIR’07, pp. 607-614, 2007.
-  Y. Sasaki, “Driving Forces in Product Selection Evaluation Sites, Magazines, andWord of Mouth Information ��� A Research on Users of a Cosmetics Product Evaluation Site,” J. of Information and Media Studies, Vol.3, No.1, pp. 29-42, 2004.
-  J. Stewart, H. Strong, J. Parker, and M. A. Bedau, “Twitter Keyword Volume, Current Spending, and Weekday Spending Norms Predict Consumer Spending,” ICDM2012 Workshops, pp. 747-753, 2012.
-  N. Glance, M. Hurst, K. Nigam, M. Siegler, R. Stockton, and T. Tomokiyo, “Deriving Marketing Intelligence from Online Discussion,” KDD’05, pp. 419-428, 2005.
-  E. Adar and L. A. Adamic, “Tracking Information Epidemics in Blogspace,” WI’05, pp. 207-214, 2005.
-  R. Kumar, J. Novak, P. Raghavan, and A. Tomkins, “On the Bursty Evolution of Blogspace,” WWW’03, pp. 568-576, 2003.
-  B. L. Tseng, J. Tatemura, and Y. Wu, “Tomographic Clustering to Visualize Blog Communities as Mountain Views,” WWW2005 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2005.
-  M. Gamon and A. Aue (Eds.), Proc. Workshop on Sentiment and Subjectivity in Text, 2006.
-  J. G. Shanahan, Y. Qu, and J. Wiebe, “Computing Attitude and Affect in Text: Theory and Applications,” Springer, 2006.
-  V. Hatzivassiloglou and K. R. McKeown, “Predicting the Semantic Orientation of Adjectives,” Proc. ACL97, pp. 174-181, 1997.
-  J. Broß and H. Ehrig, “Generating Context-Aware Sentiment Lexicon for Aspect-Based Product Review Mining,” WI-IAT2010, pp. 435-439, 2010.
-  C. Whitelaw, N. Garg, and S. Argamon, “Using Appraisal Groups for Sentiment Analysis,” CIKM2005, pp. 625-631, 2005.
-  B. Pang, L. Lee, and S. Vaihyanathan, “Thumbs Up? Sentiment Classification UsingMachine Learning Techniques,” Proc. EMNLP, pp. 79-86, 2002.
-  P. D. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” Proc. ACL, pp. 417-424, 2002.
-  T. Hirayama, T. Yumoto, M. Nii, and Y. Takahashi, “Extraction and Presentation of Product Reputation by Attribute Evaluation Model,” DEIM2011, F2-5, 2011 (in Japanese).
-  T. Konishi, T. Tezuka, F. Kimura, and A. Maeda, “Review Portfolio by Statistical Methods,” DEIM2010, A9-4, 2010 (in Japanese).
-  R. Seto and T. Satoh, “Feature-Based Reputation Browser Using Product Descriptions,” DEIM2009, C6-5, 2009 (in Japanese).
-  S. T. K. Lam and J. Riedl, “Shilling Recommender Systems for Fun and Profit,” Proc. 13th Int. Conf. on World Wide Web, pp. 393-402, 2004.