Abstract
Sentiment word identification (SWI) refers to the task of automatically identifying whether a given word expresses positive or negative opinion. SWI is a critical component of sentiment analysis technologies. Traditional sentiment word identification techniques become unqualified because they need seed sentiment words which leads to low robustness. In this paper, we consider SWI as a matrix factorization problem and propose three models for it. Instead of seed words, we exploit sentiment matching and sentiment consistency for modeling. Extensive experimental studies on three real-world datasets demonstrate that our models outperform the state-of-the-art approaches.
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References
Bross, J., Ehrig, H.: Automatic construction of domain and aspect specific sentiment lexicons for customer review mining. In: CIKM, pp. 1077–1086 (2013)
Ding, X., Liu, B., Yu, P.: A holistic lexicon-based approach to opinion mining. In: WSDM, pp. 231–240 (2008)
Ganesan, K., Zhai, C.X., Viegas, E.: Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. In: WWW, pp. 869–878 (2012)
Golub, G., Reinsch, C.E.: Singular value decomposition and least squares solutions. Numerische Mathematik 14(5), 403–420 (1970)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: ACL, pp. 174–181 (1997)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: 10th Proceeding of ACM SIGKDD, pp. 168–177 (2004)
Kamps, J., Marx, M., Mokken, R.J., De Rijke, M.: Using WordNet to measure semantic orientation of adjectives. In: LREC, pp. 1115–1118 (2004)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Liang, J.G., Zhou, X.F., Hu, Y., Guo, L., Bai, S.: CONR: A novel method for sentiment word identification. In: CIKM, pp. 1943–1946 (2014)
Lu, Y., Castellanos, M., Dayal, U., Zhai, C.X.: Automatic construction of a context-aware sentiment lexicon: An optimization approach. In: WWW, pp. 347–356 (2011)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: ACL, pp. 142–150 (2011)
Miller, G.A.: WordNet: A lexical database for English. Communication of the ACM 38(11), 39–41 (1995)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)
Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: EMNLP, pp. 339–346 (2005)
Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: IJCAI, pp. 1199–1204 (2009)
Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: EACL, pp. 675–682 (2009)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21(4), 315–346 (2003)
Vechtomova, O., Suleman, K., Thomas, J.: An information retrieval-based approach to determining contextual opinion polarity of words. In: ECIR, pp. 507–512 (2014)
Yu, H., Deng, Z.H., Li, S.: Identifying sentiment words using an optimization-based model without seed words. In: ACL, pp. 855–859 (2013)
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Liang, J., Zhou, X., Hu, Y., Guo, L., Bai, S. (2015). Sentiment Word Identification with Sentiment Contextual Factors. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_18
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DOI: https://doi.org/10.1007/978-3-319-25255-1_18
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