Abstract
Topic-specific opinion summarization (TOS) plays an important role in helping users digest online opinions, which targets to extract a summary of opinion expressions specified by a query, i.e. topic-specific opinionated information (TOI). A fundamental problem in TOS is how to effectively represent the TOI of an opinion so that salient opinions can be summarized to meet user’s preference. Existing approaches for TOS are either limited by the mismatch between topic-specific information and its corresponding opinionated information or lack of ability to measure opinionated information associated with different topics, which in turn affect the performance seriously. In this paper, we represent TOI by word pair and propose a weighting scheme to measure word pair. Then, we integrate word pair into a random walk model for opinionated sentence ranking and adopt MMR method for summarization. Experimental results showed that salient opinion expressions were effectively weighted and significant improvement achieved for TOS.
Keywords
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References
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD 2004 (2004)
Huang, X., Croft, W.: A Unified Relevance Model for Opinion Retrieval. In: Proceedings of CIKM (2009)
Na, S.-H., Lee, Y., Nam, S.-H., Lee, J.-H.: Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 734–738. Springer, Heidelberg (2009)
Zhang, M., Ye, X.: A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In: SIGIR 2008 (2008)
Yang, K., Yu, N., Valerio, A., Zhang, H.: WIDIT in TREC-2006 Blog track. In: Proceedings of TREC (2006)
Li, B., Zhou, L., Feng, S., Wong, K.F.: A unified graph model for sentence-based opinion retrieval. In: ACL 2010 (2010)
Dang, H.: Overview of the TAC 2008 opinion question answering and summarization tasks. In: TAC 2008 (2008)
Stoyanov, V., Cardie, C.: Toward opinion summarization: linking the sources. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text (2006)
He, B., Macdonald, C., He, J., Ounis, I.H.: An effective statistical approach to blog post opinion retrieval. In: CIKM 2008 (2008)
Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: ACL 2002 (2002)
Amati, G.: Probabilistic models for information retrieval based on Divergence from Randomness. PhD thesis, University of Glasgow (2003)
Kim, J., Li, J., Lee, J.: Discovering the discriminative views: Measuring term weights for sentiment analysis. In: ACL-IJCNLP 2009 (2009)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: LREC (2006)
Erkan, G., Radev, D.R.: Lexpagerank: Prestige in multi-document text summarization. In: EMNLP 2004 (2004)
Macdonald, C., Ounis, I.: Overview of the TREC-2006 Blog Track. In: TREC 2006 (2006)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: EMNLP 2005 (2005)
Xia, Y., Xu, R., Wong, K.F., Feng: The unified collocation framework for opinion mining. In: International Conference on Machine Learning and Cybernetics 2007 (2007)
Varma, V., Pingali, P., Katragadda, R., Krishna, S., Ganesh, S., Sarvabhotla, K., Garapati, H., Gopisetty, H., Reddy, V.B., Reddy, K., Bysani, P., Bharadwaj, R.: IIIT Hyderabad at TAC 2008. In: TAC 2008 (2008)
Li, F., Tang, Y., Huang, M., Zhu, X.: Answering opinion questions with random walks on graphs. In: ACL 2009 (2009)
Lerman, K., Godensohn, S., McDonald, R.: Sentiment Summarization: Evaluating and learning User Preferences. In: EACL 2009 (2009)
Paul, M., Zhai, C., Girju, R.: Summarizing contrastive viewpoints in opinionated text. In: EMNLP 2010 (2010)
Zhao, X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: EMNLP 2010 (2010)
Lin, C., He, Y., Everson, R.: A comparative study of Bayesian models for unsupervised sentiment detection. In: ACL 2010 (2010)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boomboxes and blenders: Domain adaptationfor sentiment classification. In: ACL (2007)
Ernsting, B., Weerkamp, W., de Rijke, M.: Language modeling approaches to blog post and feed finding. In: TREC 2007 (2007)
Zhou, L., Li, B., Gao, W., Wei, Z., Wong, K.F.: Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities. In: Proceedings of EMNLP 2011 (Oral presentation), Edinburgh, Scotland, July 27-31 (2011)
Xu, R., Wong, K.F., Xia, Y.: Opinmine - Opinion Analysis System by CUHK for NTCIR-6 Pilot Task. In: Proceedings of NTCIR-6
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Li, B., Zhou, L., Gao, W., Wong, KF., Wei, Z. (2011). An Effective Approach for Topic-Specific Opinion Summarization. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_36
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DOI: https://doi.org/10.1007/978-3-642-25631-8_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25630-1
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