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Sentiment Analysis on Twitter Data for Portuguese Language

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7243))

Abstract

This work presents an study on Sentiment Analysis on Twitter data for the Portuguese language. It evaluates the impact of different preprocessing techniques, Portuguese polarity lexicons and negation models showing low impact of preprocessing and negation modelling in classification of tweets.

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References

  1. Asher, N., Benamara, F., Mathieu, Y.: Appraisal of opinion expressions in discourse. Lingvisticæ Investigationes 31.2, 279–292 (2009)

    Article  Google Scholar 

  2. Calais Guerra, P.H., Veloso, A., Meira Jr., W., Almeida, V.: From bias to opinion: a transfer-learning approach to real-time sentiment analysis. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 150–158. ACM, New York (2011), http://doi.acm.org/10.1145/2020408.2020438

    Chapter  Google Scholar 

  3. Choi, Y., Cardie, C.: Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: EMNLP 2008, pp. 793–801. ACL, Stroudsburg (2008)

    Chapter  Google Scholar 

  4. Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: COLING 2010, pp. 241–249. ACL, Stroudsburg (2010)

    Google Scholar 

  5. Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: CHI 2010, pp. 1195–1198. ACM, New York (2010)

    Google Scholar 

  6. Ding, X., Liu, B., Zhang, L.: Entity discovery and assignment for opinion mining applications. In: KDD 2009, pp. 1125–1134. ACM, New York (2009)

    Chapter  Google Scholar 

  7. Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.A.: Part-of-speech tagging for twitter: Annotation, features, and experiments. In: ACL 2011 (Short Papers), pp. 42–47. ACL (2011)

    Google Scholar 

  8. Go, A., Huang, L., Bhayani, R.: Twitter sentiment analysis. Entropy, 17 (2009)

    Google Scholar 

  9. Golder, S.A., Macy, M.W.: Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures. Science 333(6051), 1878–1881 (2011)

    Article  Google Scholar 

  10. Grefenstette, G., Qu, Y., Shanahan, J.G., Evans, D.A.: Coupling niche browsers and affect analysis for an opinion mining application. In: RIAO 2004, pp. 186–194. CID (2004)

    Google Scholar 

  11. Han, B., Baldwin, T.: Lexical normalisation of short text messages: Makn sens a #twitter. In: ACL-HLT 2011 (2011)

    Google Scholar 

  12. Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology 60(11), 2169–2188 (2009)

    Article  Google Scholar 

  13. Kamps, J., Marx, M., Mokken, R.J., de Rijke, M.: Using WordNet to measure semantic orientation of adjectives. In: LREC 2004 (2004)

    Google Scholar 

  14. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: Artificial Intelligence, pp. 538–541 (2011)

    Google Scholar 

  15. Moilanen, K., Pulman, S.: Sentiment composition. In: RANLP 2007, Borovets, Bulgaria, pp. 378–382 (2007)

    Google Scholar 

  16. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. Computer, 1320–1326 (2010)

    Google Scholar 

  17. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL, pp. 271–278 (2004)

    Google Scholar 

  18. Riloff, E., Wiebe, J., Phillips, W.: Exploiting subjectivity classification to improve information extraction. In: AAAI 2005 (2005)

    Google Scholar 

  19. Schwenter, S.A.: The pragmatics of negation in Brazilian Portuguese. Lingua 115(10), 1427–1456 (2005)

    Article  Google Scholar 

  20. Silva, I.S., Gomide, J., Veloso, A., Meira Jr., W., Ferreira, R.: Effective sentiment stream analysis with self-augmenting training and demand-driven projection. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information, SIGIR 2011, pp. 475–484. ACM, New York (2011), http://doi.acm.org/10.1145/2009916.2009981

    Chapter  Google Scholar 

  21. Silva, M.J., Team, R.: Notas sobre a realizao e qualidade do twitómetro. Tech. rep., University of Lisbon, Faculty of Sciences, LASIGE (May 2011)

    Google Scholar 

  22. Silva, M.J., Carvalho, P., Costa, C., Sarmento, L.: Automatic expansion of a social judgment lexicon for sentiment analysis. Technical Report TR 1008 University of Lisbon Faculty of Sciences LASIGE (2010)

    Google Scholar 

  23. Souza, M., Vieira, R., Busetti, D., Chishman, R., Alves, I.M.: Construction of a portuguese opinion lexicon from multiple resources. In: STIL 2011, Cuiabá, Brazil (2011)

    Google Scholar 

  24. Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: ACL 2002, pp. 417–424. Association for Computational Linguistics, Morristown (2002)

    Google Scholar 

  25. Wiegand, M., Balahur, A., Roth, B., Klakow, D.: A survey on the role of negation in sentiment analysis. Imagine, 60–68 (July 2010)

    Google Scholar 

  26. Wilson, T., Wiebe, J., Hwa, R.: Recognizing strong and weak opinion clauses. Computational Intelligence 22, 73–99 (2006)

    Article  MathSciNet  Google Scholar 

  27. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 1533–1541. Association for Computational Linguistics, Singapore (2009), http://www.aclweb.org/anthology/D/D09/D09-1159

    Google Scholar 

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Souza, M., Vieira, R. (2012). Sentiment Analysis on Twitter Data for Portuguese Language. In: Caseli, H., Villavicencio, A., Teixeira, A., Perdigão, F. (eds) Computational Processing of the Portuguese Language. PROPOR 2012. Lecture Notes in Computer Science(), vol 7243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28885-2_28

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  • DOI: https://doi.org/10.1007/978-3-642-28885-2_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28884-5

  • Online ISBN: 978-3-642-28885-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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