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Integrating Intra-and Inter-document Evidences for Improving Sentence Sentiment Classification 被引量：6

Integrating Intra-and Inter-document Evidences for Improving Sentence Sentiment Classification
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作者 ZHAO Yan-Yan QIN Bing LIU Ting
机构地区 Research Center for Information Retrieval
出处 《自动化学报》 EI CSCD 北大核心 2010年第10期1417-1425,共9页 Acta Automatica Sinica
基金 Supported by National High Technology Research and Development Program of China （863 Program） （2008AA01Z144） National Natural Science Foundation of China （60803093 60975055）
关键词 数码相机 像素 富士 光学变焦 Sentence sentiment classification sentiment analysis intra-document evidence inter-document evidence graph-based propagation approach
分类号 TB852.1 [一般工业技术—摄影技术]
  • 相关文献

参考文献17

  • 1Gamon M. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the 20th Interna- tional Conference on Computational Linguistics. Geneva, Switzerland: Association for Computational Linguistics, 2004. 841-847.
  • 2Kim S M, Hovy E. Automatic identification of pro and con reasons in online reviews. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics. Sydney, Australia: Association for Computational Linguistics, 2006. 483-490.
  • 3Zhao J, Liu K, Wang G. Adding redundant features for CRFs-based sentence sentiment classification. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Hawaii, USA: Association for Computational Linguistics, 2008. 117-126.
  • 4Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, USA: ACM. 2004. 168-177.
  • 5Kim S M, Hovy E. Automatic detection of opinion bearing words and sentences. In: Proceedings of the 2nd International Joint Conference on Natural Language Processing. Jeju Island, Korea: Springer, 2005. 61-66.
  • 6Yu H, Hatzivassiloglou V. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. Sapporo, Japan: Association for Computational Linguistics, 2003. 129-136.
  • 7Wu F Y. The potts model. Reviews of Modern Physics, 1982, 54(1): 235-268.
  • 8Pang B, Lee L. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics. Barcelona, Spain: Association for Computational Linguistics, 2004. 271-278.
  • 9McDonald R, Hannan K, Neylon T, Wells M, Reynar J. Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Prague, Czech Republic: Association for Computational Linguistics, 2007. 432-439.
  • 10Tanaka K, Morita T. Application of cluster variation method to image restoration problem. In: Proceedings of the Theory and Applications of the Cluster Variation and Path Probability Methods. New York, USA: Association for Computational Linguistics, 1996. 353-373.

同被引文献26

  • 1LIU B. Sentiment analysis and subjectivity[M]. 2nd ed. Handbook of Natural Language Processing. Florida: CRC Press, 2010: 1-38.
  • 2Scheible C. Sentiment translation through lexicon induction [C] //Proceedings of the ACL Student Research Workshop, 2010: 25-30.
  • 3JIANG L, Yu M, ZHOU M, et al. Target-dependent twitter sentiment classification [C] //Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, 2011:151-160.
  • 4Prabowo R, Thelwall M. Sentiment analysis: A combined ap- proach [J]. Journal of Informetries, 2009, 3 (l): 143-157.
  • 5ZHAO J, LIU K, WANG G. Adding redundant features for CRFs-based sentence sentiment classification [C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2008: 117-126.
  • 6Balahur A, Hermida JM, Montoyo A. Detecting implicit ex pressions of sentiment in text based on commonsense knowledge [C] //Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, 2011: 53-60.
  • 7Davidov D, Tsur O, Rappoport A. Semi-supervised recognition of sarcastic sentences in twitter and Amazon [C]//Proceeding of the Conference on Computational Linguistics, 2010: 107-116.
  • 8ZHANG L, LIU B. Identifying noun product features that imply opinions [C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, 2011 : 575-580.
  • 9DING XW, LIU B, YU PS. A holistic lexlcon-based ap- proach to opinion mining [C]//Proceedings of the 1st ACM International Conference on Web Search and Web Data Mining, 2008: 231-239.
  • 10Greene S, Resnik P. More than words: Syntactic packaging and implicit sentiment[C]//Proceedings of the Annual Conference of the North American Chapter of the ACL, 2009: 503-511.

引证文献6

二级引证文献63

自动化学报
2010年 第10期

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