Abstract
Sentiment analysis has become an actively researched area recently, which aims to detect positive and negative opinions in texts. A good indicator for the polarity of a given text is the number of words in it that have positive or negative meanings. The so called sentiment lexicons are lists containing words together with their polarities. In this paper we present methods for creating sentiment lexicons automatically. We use these lexicons in sentiment analysis tasks on general and domain-specific Hungarian corpora. We compare the efficiency of sentiment lexicons from different domains and show the importance of using domain-specific sentiment lexicons for different sentiment analysis tasks.
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References
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC 2010 (2010)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)
Miháltz, M.: OpinHuBank: szabadon hozzáférhetÅ‘ annotált korpusz magyar nyelvű véleményelemzéshez. In: IX. Magyar SzámÃtógépes Nyelvészeti Konferencia, pp. 343–345 (2013)
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS) 21(4), 315–346 (2003)
Miháltz, M., Hatvani, C., Kuti, J., Szarvas, G., Csirik, J., Prószéky, G., Váradi, T.: Methods and results of the Hungarian WordNet project. In: Proceedings of the Fourth Global WordNet Conference, GWC 2008. Citeseer (2008)
Brown, P.F., Desouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Computational Linguistics 18(4), 467–479 (1992)
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Hangya, V. (2015). Automatic Construction of Domain Specific Sentiment Lexicons for Hungarian. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_21
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DOI: https://doi.org/10.1007/978-3-319-24033-6_21
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