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A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval

Published: 20 July 2008 Publication History

Abstract

Opinion retrieval is a task of growing interest in social life and academic research, which is to find relevant and opinionate documents according to a user's query. One of the key issues is how to combine a document's opinionate score (the ranking score of to what extent it is subjective or objective) and topic relevance score. Current solutions to document ranking in opinion retrieval are generally ad-hoc linear combination, which is short of theoretical foundation and careful analysis. In this paper, we focus on lexicon-based opinion retrieval. A novel generation model that unifies topic-relevance and opinion generation by a quadratic combination is proposed in this paper. With this model, the relevance-based ranking serves as the weighting factor of the lexicon-based sentiment ranking function, which is essentially different from the popular heuristic linear combination approaches. The effect of different sentiment dictionaries is also discussed. Experimental results on TREC blog datasets show the significant effectiveness of the proposed unified model. Improvements of 28.1% and 40.3% have been obtained in terms of MAP and p@10 respectively. The conclusion is not limited to blog environment. Besides the unified generation model, another contribution is that our work demonstrates that in the opinion retrieval task, a Bayesian approach to combining multiple ranking functions is superior to using a linear combination. It is also applicable to other result re-ranking applications in similar scenario.

References

[1]
Dong, Z. HowNet. http://www.HowNet.org
[2]
Eguchi, K. and Lavrenko, V. Sentiment Retrieval using Generative Models. In Proceedings of Empirical Methods on Natural Language Processing (EMNLP) 2006, 345--354.
[3]
Esuli, A. and Sebastiani, F. Determining the semantic orientation of terms through gloss classification. In Proceedings of CIKM 2005, 617--624.
[4]
Hurst, M. and Nigam, K. Retrieving Topical Sentiments from Online Document Collections. Document Recognition and Retrieval XI. 27--34. 2004.
[5]
Lafferty, J. and Zhai, C. Probabilistic relevance models based on document and query generation. Language Modeling and Information Retrieval, Kluwer International Series on Information Retrieval, Vol. 13, 2003.
[6]
Liao, X., Cao, D., Tan, S., Liu, Y., Ding, G., and Cheng X. Combining Language Model with Sentiment Analysis for Opinion Retrieval of Blog-Post. Online Proceedings of Text Retrieval Conference (TREC) 2006. http://trec.nist.gov/
[7]
Liu, B., Hu, M., and Cheng, J. Opinion observer: analyzing and comparing opinions on the Web. WWW 2005: 342--351
[8]
Mei, Q., Ling, X., Wondra, M., Su, H., and Zhai, C. Topic sentiment mixture: modeling facets and opinions in weblogs. WWW 2007: 171--180
[9]
Metzler, D., Strohman T., Turtle H., and Croft, W.B. Indri at TREC 2004: Terabyte Track. Online Proceedings of 2004 Text REtrieval Conference (TREC 2004), 2004
[10]
Mishne, G. Multiple Ranking Strategies for Opinion Retrieval in Blogs. Online Proceedings of TREC, 2006.
[11]
Oard, D., Elsayed, T., Wang, J., and Wu, Y. TREC-2006 at Maryland: Blog, Enterprise, Legal and QA Tracks. Online Proceedings of TREC, 2006. http://trec.nist.gov/
[12]
Ounis, I., de Rijke, M., Macdonald, C., Mishne, G., and Soboroff, I. Overview of the TREC 2006 Blog Track. In Proceedings of TREC 2006, 15--27. http://trec.nist.gov/
[13]
Pang, B., et al, Thumbs up? Sentiment Classification Using Machine Learning Techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) 2002, 79--86.
[14]
Stone, P., Dunphy, D., Smith, M., and Ogilvie, D. The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge, 1966.
[15]
Tong, R. 2001. An Operational System for Detecting and Tracking Opinions in on-line discussion. SIGIR Workshop on Operational Text Classification. 2001. 1--6.
[16]
Turtle, H. and Croft, W.B. Evaluation of an Inference Network-Based Retrieval Model. ACM Transactions on Information System, in 9(3),187--222, 1991.
[17]
Wilson, T., Wiebe, J., and Hoffmann, P. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of HLT/EMNLP 2005. 347--354.
[18]
WordNet. http://wordnet.princeton.edu/
[19]
Yang, K., Yu, N., Valerio, A., Zhang, H. WIDIT in TREC-2006 Blog track. Online Proceedings of TREC, 2006. http://trec.nist.gov/
[20]
Zhai, C. and Lafferty, J. A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems (ACM TOIS ), Vol. 22, No. 2, 179--214.2004.
[21]
Zhai, C. A Brief Review of Information Retrieval Models, Technical report, Dept. of Computer Science, UIUC, 2007
[22]
Zhang, W. and Yu, C. UIC at TREC 2006 Blog Track. Online Proceedings of TREC, 2006. http://trec.nist.gov/
[23]
Mishne, G. and Glance, N. Leave a Reply: An analysis of Weblog Comments. In WWE 2006 (WWW 2006 Workshop on Weblogging Ecosystem), 2006.
[24]
Mishne, G. Using blog properties to improve retrieval, In Proceedings of the International Conference on Weblogs and. Social Media (ICSWM) 2007.
[25]
Singhal, A. Modern information retrieval: A brief overview. Bulletin of the IEEE Computer Society Technical committee on Data Engineering, 24(4):35--43, 2001.
[26]
Macdonald, C. and Ounis, I. Overview of the TREC-2007 Blog Track. Online Proceedings of the 16th Text Retrieval Conference (TREC2007). http://trec.nist.gov/pubs/trec16/t16_proceedings.html

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    cover image ACM Conferences
    SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
    July 2008
    934 pages
    ISBN:9781605581644
    DOI:10.1145/1390334
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    Published: 20 July 2008

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    Author Tags

    1. generation model
    2. opinion generation model
    3. opinion retrieval
    4. sentiment analysis
    5. topic relevance

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