Takayoshi Okamoto
ABSTRACT
Recently, a number of documents are published on the web. One of the crucial techniques to access to such information is sentiment sentence retrieval. It is very useful to retrieve positive or negative opinions to a specific topic at sentence level. Considering the property that sentiment polarities are often locally consistent in a document, we focus on using local context information for retrieving sentiment-bearing sentences. For this objective, we propose a new smoothing method, extending Dirichlet prior smoothing, to improve effectiveness of the retrieval. We demonstrate through experiments that our proposed smoothing method achieves statistically significant improvements in sentiment sentence retrieval, compared with a conventional smoothing method without local context.
- R. Baeza-Yates and B. Ribeiro-Neto, editors. Modern Information Retrieval, chapter 3: Retrieval Evaluation, pages 73--97. Addison-Wesley, 1999.Google Scholar
- C. Buckley and E. M. Voorhees. Retrieval evaluation with incomplete information. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 25--32, Sheffield, UK, 2004. Google ScholarDigital Library
- K. Eguchi and V. Lavrenko. Sentiment retrieval using generative models. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 345--354, 2006. Google ScholarDigital Library
- C. Engström. Topic dependence in sentiment classification. Master's thesis, University of Cambridge, 2004.Google Scholar
- D. Hiemstra. A linguistically motivated probabilistic model of information retrieval. In Research and Advanced Technology for Digital Libraries, volume 1513 of Lecture Notes in Computer Science, pages 569--584. Springer-Verlag, 1998. Google ScholarDigital Library
- R. Krovetz. Viewing morphology as an inference process. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 191--202, Pittsburgh, Pennsylvania, USA, 1993. Google ScholarDigital Library
- V. Lavrenko and W. B. Croft. Relevance based language models. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 120--127, New Orleans, Louisiana, USA, 2001. Google ScholarDigital Library
- C. Macdonald, I. Ounis, and I. Soboroff. Overview of the TREC-2007 blog track. In Proceedings of the 16th Text REtrieval Conference (TREC 2007). NIST Special Publication 500-274, Nov. 2007.Google Scholar
- Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai. Topic sentiment mixture: Modeling facets and opinions in weblogs. In Proceedings of the 16th International Conference on World Wide Web, pages 171--180, Banff, Alberta, Canada, May 2007. Google ScholarDigital Library
- T. Nasukawa and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP-03), pages 70--77, Sanibel Island, Florida, USA, 2003. Google ScholarDigital Library
- I. Ounis, C. Macdonald, and I. Soboroff. Overview of the TREC-2008 blog track. In Proceedings of the 17th Text REtrieval Conference (TREC 2008). NIST Special Publication 500--277, Nov. 2008.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
- J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 275--281, Melbourne, Australia, 1998. Google ScholarDigital Library
- J. G. Shanahan, Y. Qu, and J. Wiebe, editors. Computing Attitude and Affect in Text. Springer, Dordrecht, The Netherlands, 2005. Google ScholarDigital Library
- F. Song and W. B. Croft. A general language model for information retrieval. In Proceedings of the 8th ACM International Conference on Information and Knowledge Management, pages 316--321, Kansas City, Missouri, USA, 1999. Google ScholarDigital Library
- J. Wiebe, T. Wilson, and C. Cardie. Annotating expressions of opinions and emotions in language. Language resources and Evaluation, 39(2-3):165--210, 2005.Google Scholar
- T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada, 2005. Google ScholarDigital Library
- J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining, pages 427--434, Melbourne, Florida, USA, 2003. Google ScholarDigital Library
- C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 334--342, New Orleans, Louisiana, USA, 2001. Google ScholarDigital Library
- W. Zhang, L. Jia, C. Yu, and W. Meng. Improve the effectiveness of the opinion retrieval and opinion polarity classification. In Proceedings of the 17th ACM International Conference on Information and Knowledge Management, pages 1415--1416, Napa Valley, California, USA, Oct. 2008. Google ScholarDigital Library
Index Terms
- Locally contextualized smoothing of language models for sentiment sentence retrieval
Recommendations
Effective sentence retrieval based on query-independent evidence
In this paper we propose an effective sentence retrieval method that consists of incorporating query-independent features into standard sentence retrieval models. To meet this aim, we apply a formal methodology and consider different query-independent ...
Sentence Retrieval with Sentiment-specific Topical Anchoring for Review Summarization
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge ManagementWe propose Topic Anchoring-based Review Summarization (TARS), a two-step extractive summarization method, which creates review summaries from the sentences that represent the most important aspects of a review. In the first step, the proposed method ...
Using opinion-based features to boost sentence retrieval
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementOpinion mining has become recently a major research topic. A wide range of techniques have been proposed to enable opinion-oriented information seeking systems. However, little is known about the ability of opinion-related information to improve regular ...
Comments