Abstract
A lexical dictionary consisting of opinion words and their polar orientations plays a crucial contribution to opinion mining tasks (e.g., sentiment classification). Previous works on automatic construction of such dictionary have a problem of generating errors (i.e., incorrect identification of polar orientations of words in dictionary). To address the problem, this paper proposes an Error Minimization Algorithm for reducing errors caused by automatic compiling process to construct a reasonable opinion word dictionary. The proposed algorithm combines global and local resources for extracting and refining the dictionary with minimum errors. Empirical results show that our proposed approach is effective for enhancing the performance of the sentiment classification task.
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References
Dave, K., Lawerence, S., Pennock, D.M.: Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: WWW, pp. 519–528 (2003)
Ding, X., Liu, B., Yu, P.S.: A Holistic Lexicon-Based Approach to Opinion Mining. In: WSDM, pp. 231–240 (2008)
Gamon, M., Aue, A.: Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms. In: EMNLP, pp. 57–64 (2005)
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: KDD, pp. 168–177 (2004)
Kanayama, H., Nasukawa, T.: Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis. In: EMNLP, pp. 355–363 (2006)
Kamps, J., Marx, M., Mokken, R.J., Rijke, M.D.: Using WordNet to Measure Semantic Orientation of Adjectives. In: LRE, pp. 1115–1118 (2004)
Kim, S.M., Hovy, E.: Determining the Sentiment of Opinions. In: COLING, pp. 1367–1373 (2004)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: EMNLP, pp. 79–86 (2002)
Riloff, E., Wiebe, J.: Learning Extraction Patterns for Subjective Expressions. In: EMNLP, pp. 105–112 (2003)
Turney, P.D.: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL. In: ECML, pp. 491–502 (2001)
Turney, P.D.: Thumbs up or Thumbs down? Semantic Orientation Applied to Unsupervised Classifcation of Reviews. In: ACL, pp. 417–424 (2002)
Whitelaw, C., Garg, N., Argamon, S.: Using Appraisal Groups for Sentiment Analysis. In: CIKM, pp. 625–631 (2005)
Yu, H., Hatzivassigloglou, V.: Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sententces, pp. 129–136 (2003)
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Hoang, L., Lee, JT., Song, YI., Rim, HC. (2008). Combining Local and Global Resources for Constructing an Error-Minimized Opinion Word Dictionary. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_63
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DOI: https://doi.org/10.1007/978-3-540-89197-0_63
Publisher Name: Springer, Berlin, Heidelberg
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