Abstract
We show that verbs reliably represent texts when machine learning algorithms are used to learn opinions. We identify semantic verb categories that capture essential properties of human communication. Lexical patterns are applied to construct verb-based features that represent texts in machine learning experiments. Our empirical results show that expressed actions provide a reliable accuracy in learning opinions.
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Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proc Empirical Methods of Natural Language Processing EMNLP 2002, pp. 79–86 (2002)
Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Computational Intelligence 22(2), 73–99 (2006)
Thomas, M., Pang, B., Lee, L.: Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327–335 (2006)
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the of the 20th international conference on Computational Linguistics (COLING 2004), pp. 1367–1373 (2004)
Sokolova, M., Lapalme, G.: Performance measures in classification of human communication. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 159–170. Springer, Heidelberg (2007)
Halliday, M., Matthiessen, C.: An Introduction to Functional Grammar, 3rd edn. Arnold (2004)
Leech, G.: Meaning and the English Verb. Longman (2004)
Leech, G., Svartvik, J.: A Communicative Grammar of English, 3rd edn. Longman (2002)
Sokolova, M., Szpakowicz, S.: Language patterns in the learning of strategies from negotiation texts. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 288–299. Springer, Heidelberg (2006)
Biber, D., Johansson, S., Leech, G., Conrad, S., Finegan, E.: Longman Grammar of Spoken and Written English. Longman (1999)
Roget’s interactive thesaurus (2006), http://thesaurus.reference.com/
Perkins, M.: Modal Expressions in English. Ablex Publishing Corporation (1983)
Sherblom, J., Rheenen, D.V.: Spoken language indices of uncertainty. Human Communication Research 11, 221–230 (1984)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of Nineteeth National Conference on Artificial Intelligence (AAAI 2004), AAAI Press, Menlo Park (2004)
Boulle, M.: Optimal bin number for equal frequency discretizations in supervised learning. Intelligent Data Analysis 9(2), 175–188 (2005)
Witten, I., Frank, E.: Data Mining. Morgan Kaufmann, San Francisco (2005)
Feiguina, O., Lapalme, G.: Query-based summarization of customer reviews. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 452–463. Springer, Heidelberg (2007)
Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of HLTC/EMNLP 2005, pp. 339–346 (2005)
Kim, S.M., Hovy, E.: Crystal: Analyzing predictive opinions on the web. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1056–1064 (2007)
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Sokolova, M., Lapalme, G. (2008). Verbs Speak Loud: Verb Categories in Learning Polarity and Strength of Opinions. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_30
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DOI: https://doi.org/10.1007/978-3-540-68825-9_30
Publisher Name: Springer, Berlin, Heidelberg
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