Social summarization of text feedback for online auctions and interactive presentation of the summary

ABSTRACT

Buyers in online auctions write feedback comments to the sellers from whom the buyers have bought the items. Other bidders read them to determine which item to bid for. In this research, we aim at helping bidders by summarizing the feedback comments. Firstly, we examine feedback comments in online auctions. From the results of the examination, we propose a method called social summarization method, which uses social relationships in online auctions for summarizing feedback comments. We implement a system based on our method and evaluate its effectiveness. Finally, we propose an interactive presentation method of the summaries based on the result of the evaluation.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: S. R. Das and M. Y. Chen. Yahoo! for amazon: Sentiment extraction from small talk on the web. In Proceedings of 8th Asia Pacific Finance Association Annual Conference July 2001.
• 3: Kushal Dave , Steve Lawrence , David M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775226]
• 4: S. Fujimura, M. Matsumura, N. Okazaki, and M. Ishizuka. Decision-making support based on reputations on bbs. In Proceedings of 17th Annual Conference of Japanese Society for Artificial Intelligence pages 2B1--05, June 2003.
• 5: Vasileios Hatzivassiloglou , Kathleen R. McKeown, Predicting the semantic orientation of adjectives, Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, p.174-181, July 07-12, 1997, Madrid, Spain
• 6: Vasileios Hatzivassiloglou , Janyce M. Wiebe, Effects of adjective orientation and gradability on sentence subjectivity, Proceedings of the 18th conference on Computational linguistics, p.299-305, July 31-August 04, 2000, Saarbrücken, Germany [doi>10.3115/990820.990864]
• 7: N. Kobayashi, K. Inui, Y. Matsumoto, K. Tateishi, and T. Fukushima. Collecting evaluative expressions for opinion extraction. In Proceedings of 1st International Joint Conference on Natural Language Processing pages 584--589, March 2004.
• 8: Satoshi Morinaga , Kenji Yamanishi , Kenji Tateishi , Toshikazu Fukushima, Mining product reputations on the Web, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775098]
• 9: Tetsuya Nasukawa , Jeonghee Yi, Sentiment analysis: capturing favorability using natural language processing, Proceedings of the 2nd international conference on Knowledge capture, October 23-25, 2003, Sanibel Island, FL, USA [doi>10.1145/945645.945658]
• 10: C. D. Paice, Constructing literature abstracts by computer: techniques and prospects, Information Processing and Management: an International Journal, v.26 n.1, p.171-186, 1990 [doi>10.1016/0306-4573(90)90014-S]
• 11: Bo Pang , Lillian Lee , Shivakumar Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.79-86, July 06, 2002 [doi>10.3115/1118693.1118704]
• 12: T. Shibata and S. Yamada. Thesaurus Kodansha, Tokyo, 2002.
• 13: Ben Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, Proceedings of the 1996 IEEE Symposium on Visual Languages, p.336, September 03-06, 1996
• 14: E. Spertus. Smokey: Automatic recognition of hostile message. In Proceedings of Innovative Applications of Artificial Intelligence pages 1058--1065, July 1997.
• 15: P. Subasic and A. Huettner. Affect analysis of text using fuzzy semantic typing. IEEE Transaction On Fuzzy Systems Special Issue 9: 483--496, August 2001.
• 16: K. Tateishi, Y. Ishiguro, and T. Fukushima. Texts processing for intelligent access of information on the web. Journal of Japanese Society for Artificial Intelligence 19 (3):317--322, May 2004.
• 17: Peter D. Turney, Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073153]
• 18: J. Wiebe, T. Wilson, and M. Bell. Identifying collocations for recognizing opinions. In Proceedings of ACL/EACL Workshop on Collocation July 2001.
• 19: Kenji Yamanishi , Hang Li, Mining Open Answers in Questionnaire Data, IEEE Intelligent Systems, v.17 n.5, p.58-63, September 2002 [doi>10.1109/MIS.2002.1039833]
• 20: Jeonghee Yi , Tetsuya Nasukawa , Razvan Bunescu , Wayne Niblack, Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques, Proceedings of the Third IEEE International Conference on Data Mining, p.427, November 19-22, 2003

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.m Miscellaneous

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Documentation, Experimentation, Human Factors

Keywords:
feedback comments, online auction, social relationship, social summarization method, text summarization