Abstract
This paper proposes a new method of sentiment analysis for Twitter. Tweets contain various expressions; e.g., use of emoticons. The usage of these expressions links to the user’s identity and individual characters. Handling these characteristics is useful for the sentiment analysis. We focus on writing styles of each user. In this paper, we define three types of writing style; formal and two informal expressions. First, our method classifies each tweet into the three types. Then, it generates classifiers for each writing style. We apply our method to a positive / negative classification task of tweets. In the experiment, the accuracy of our method increased by approximately 3 points as compared with some baseline methods.
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References
Bar-Haim, R., Dinur, E., Feldman, R., Fresko, M., Goldstein, G.: Identifying and following expert investors in stock microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), pp. 1310–1319 (2011)
Brody, S., Diakopoulos, N.: Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! usingword lengthening to detect sentiment in microblogs. In: Proceedings of EMNLP 2011 (2011)
Burger, J.D., Henderson, J., Kim, G., Zarrella, G.: Discriminating gender on twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), pp. 1301–1309 (2011)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report, Stanford University (2009)
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (2011)
Koppel, M., Argamon, S., Shimoni, A.R.: Automatically categorizing written texts by author gender. Literary and Linguistic Computing 17, 401–412 (2003)
Pang, B., Lee, L.: Opinion mining and sentiment analysis, vol. 2. Foundations and Trends in Information Retrieval (2008)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, pp. 37–44 (2010)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 (2003)
Takamura, H., Inui, T., Okumura, M.: Extracting semantic orientations of words using spin model. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 133–140 (2005)
Turney, P.D.: Thumbs up? or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424 (2002)
Wiebe, J., Riloff, E.: Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)
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Maeda, H., Shimada, K., Endo, T. (2012). Twitter Sentiment Analysis Based on Writing Style. In: Isahara, H., Kanzaki, K. (eds) Advances in Natural Language Processing. JapTAL 2012. Lecture Notes in Computer Science(), vol 7614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_28
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DOI: https://doi.org/10.1007/978-3-642-33983-7_28
Publisher Name: Springer, Berlin, Heidelberg
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