Advertisement

Emotion Tokens: Bridging the Gap among Multilingual Twitter Sentiment Analysis

  • Anqi Cui
  • Min Zhang
  • Yiqun Liu
  • Shaoping Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7097)

Abstract

Twitter is a microblogging service where worldwide users publish their feelings. However, sentiment analysis for Twitter messages (tweets) is regarded as a challenging problem because tweets are short and informal. In this paper, we focus on this problem by the analysis of emotion tokens, including emotion symbols (e.g. emoticons), irregular forms of words and combined punctuations. According to our observation on five million tweets, these emotion tokens are commonly used (0.47 emotion tokens per tweet). They directly express one’s emotion regardless of his language; hence become a useful signal for sentiment analysis on multilingual tweets. Firstly, emotion tokens are extracted automatically from tweets. Secondly, a graph propagation algorithm is proposed to label the tokens’ polarities. Finally, a multilingual sentiment analysis algorithm is introduced. Comparative evaluations are conducted among semantic lexicon based approach and some state-of-the-art Twitter sentiment analysis Web services, both on English and non-English tweets. Experimental results show effectiveness of the proposed algorithms.

Keywords

Multilingual sentiment analysis Twitter sentiment analysis Emotion token Sentiment lexicon Network informal language 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, pp. 2200–2204 (2010)Google Scholar
  2. 2.
    Banea, C., Mihalcea, R., Wiebe, J.: A bootstrapping method for building subjectivity lexicons for languages with scarce resources. In: Proc. LREC 2008 (2008)Google Scholar
  3. 3.
    Banea, C., Mihalcea, R., Wiebe, J.: Multilingual subjectivity: are more languages better? In: Proc. 23rd COLING Conference, pp. 28–36 (2010)Google Scholar
  4. 4.
    Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Coling 2010: Posters, Beijing, China, pp. 36–44 (2010)Google Scholar
  5. 5.
    Bautin, M., Vijayarenu, L., Skiena, S.: International sentiment analysis for news and blogs. In: Proc. International Conference on Weblogs and Social Media (2008)Google Scholar
  6. 6.
    Bifet, A., Frank, E.: Sentiment Knowledge Discovery in Twitter Streaming Data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 1–15. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Boiy, E., Moens, M.F.: A machine learning approach to sentiment analysis in multilingual web texts. Information Retrieval 12, 526–558 (2009)CrossRefGoogle Scholar
  8. 8.
    Bollen, J., Pepe, A., Mao, H.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. arXiv:0911.1583 (2009)Google Scholar
  9. 9.
    Boyd-Graber, J., Resnik, P.: Holistic sentiment analysis across languages: multilingual supervised latent Dirichlet allocation. In: EMNLP 2010, pp. 45–55 (2010)Google Scholar
  10. 10.
    Brody, S., Diakopoulos, N.: Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! using word lengthening to detect sentiment in microblogs. In: EMNLP 2011, pp. 562–570 (2011)Google Scholar
  11. 11.
    Denecke, K.: Using SentiWordNet for multilingual sentiment analysis. In: IEEE 24th International Conference on Data Engineering Workshop, pp. 507–512 (2008)Google Scholar
  12. 12.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Tech. rep., Stanford CS224N Project (2009)Google Scholar
  13. 13.
    Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Micro-blogging as online word of mouth branding. In: CHI 2009, pp. 3859–3864 (2009)Google Scholar
  14. 14.
    Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proc. 49th ACL: HLT, vol. 1, pp. 151–160 (2011)Google Scholar
  15. 15.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the First Workshop on Online Social Networks, pp. 19–24 (2008)Google Scholar
  16. 16.
    Li, Z., Zhang, M., Ma, S., Zhou, B., Sun, Y.: Automatic Extraction for Product Feature Words from Comments on the Web. In: Lee, G.G., Song, D., Lin, C.-Y., Aizawa, A., Kuriyama, K., Yoshioka, M., Sakai, T. (eds.) AIRS 2009. LNCS, vol. 5839, pp. 112–123. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, 2nd edn. CRC Press, Taylor and Francis Group (2010)Google Scholar
  18. 18.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Sentiful: A lexicon for sentiment analysis. IEEE Transactions on Affective Computing 2(1), 22–36 (2011)CrossRefGoogle Scholar
  19. 19.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC 2010 (2010)Google Scholar
  20. 20.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  21. 21.
    Semiocast: Half of messages on twitter are not in english. Tech. rep. (2010)Google Scholar
  22. 22.
    Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1556–1560 (2008)Google Scholar
  23. 23.
    Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Tech. rep., CMU-CALD-02-107 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anqi Cui
    • 1
    • 2
    • 3
  • Min Zhang
    • 1
    • 2
    • 3
  • Yiqun Liu
    • 1
    • 2
    • 3
  • Shaoping Ma
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Intelligent Technology and SystemsTsinghua Univ.BeijingChina
  2. 2.Tsinghua National Laboratory for Information Science and TechnologyTsinghua Univ.BeijingChina
  3. 3.Dept. of Computer Science and TechnologyTsinghua Univ.BeijingChina

Personalised recommendations