Abstract
This paper presents a new partially supervised approach to phrase-level sentiment analysis that first automatically constructs a polarity-tagged corpus and then learns sequential sentiment tag from the corpus. This approach uses only sentiment sentences which are readily available on the Internet and does not use a polarity-tagged corpus which is hard to construct manually. With this approach, the system is able to automatically classify phrase-level sentiment. The result shows that a system can learn sentiment expressions without a polarity-tagged corpus.
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References
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: EMNLP 2002: Proceedings of the ACL 2002 conference on Empirical methods in natural language processing, Morristown, NJ, USA, pp. 79–86. Association for Computational Linguistics (2002)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: ACL 2002: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Morristown, NJ, USA, pp. 417–424. Association for Computational Linguistics (2001)
Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: K-CAP 2003: Proceedings of the 2nd international conference on Knowledge capture, pp. 70–77. ACM, New York (2003)
Fei, Z., Liu, J., Wu, G.: Sentiment classification using phrase patterns, pp. 1147–1152. IEEE Computer Society, Los Alamitos (2004)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: HLT 2005: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Morristown, NJ, USA, pp. 347–354. Association for Computational Linguistics (2005)
Breck, E., Choi, Y., Cardie, C.: Identifying expressions of opinion in contenxt. In: Proceedings of the Twentieth International Join Conference on Artificial Intelligence (2007)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, Morristown, NJ, USA, pp. 174–181. Association for Computational Linguistics (1997)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association, vol. 21, pp. 315–346. ACM Press, New York (2003)
Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss classification. In: CIKM 2005: Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 617–624. ACM, New York (2005)
John, L., McCallum, A., Pereira, F.: Conditional random fields:probabilistic models for segmenting and labeling sequence data. In: 18th International Conference on Machine Learning (2001)
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Nam, SH., Na, SH., Kim, J., Lee, Y., Lee, JH. (2009). Partially Supervised Phrase-Level Sentiment Classification. In: Li, W., Mollá-Aliod, D. (eds) Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. ICCPOL 2009. Lecture Notes in Computer Science(), vol 5459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00831-3_21
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DOI: https://doi.org/10.1007/978-3-642-00831-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00830-6
Online ISBN: 978-3-642-00831-3
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