Usefulness of Sentiment Analysis

  • Jussi Karlgren
  • Magnus Sahlgren
  • Fredrik Olsson
  • Fredrik Espinoza
  • Ola Hamfors
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

What can text sentiment analysis technology be used for, and does a more usage-informed view on sentiment analysis pose new requirements on technology development?

Keywords

Sentiment Analysis Lexical Item Human Emotion Computational Linguistics Lexical Resource 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In: Annual Conference of the Association of Computational Linguistics, ACL (2007)Google Scholar
  2. 2.
    Bollen, J., Mao, H., Zeng, X.J.: Twitter mood predicts the stock market. Journal of Computational Science 2, 1–8 (2010)CrossRefGoogle Scholar
  3. 3.
    Bollen, J., Pepe, A., Mao, H.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of the WWW Conference (2010)Google Scholar
  4. 4.
    Brody, D.C., Hughston, L.P., Macrina, A.: Credit risk, market sentiment and randomly-timed default. In: Crisan, D. (ed.) Stochastic Analysis. Springer, Heidelberg (2010)Google Scholar
  5. 5.
    Chanel, G., Rebetez, C., Bétrancourt, M., Pun, T.: Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. In: MindTrek 2008. ACM, New York (2008)Google Scholar
  6. 6.
    Darwin, C.: The Expression of the Emotions in Man and Animals. John Murray, London (1872)CrossRefGoogle Scholar
  7. 7.
    Dunker, P., Nowak, S., Begau, A., Lanz, C.: Content-based mood classification for photos and music: a generic multi-modal classification framework and evaluation approach. In: MIR 2008: Proceeding of the 1st ACM International Conference on Multimedia Information Retrieval. ACM, New York (2008)Google Scholar
  8. 8.
    Ekman, P.: An argument for basic emotions. In: Cognition and Emotion, pp. 169–200 (1992)Google Scholar
  9. 9.
    James, W.: What is an emotion? Mind, 188–205 (1884)Google Scholar
  10. 10.
    Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. JASIST 60, 2169–2188 (2009)CrossRefGoogle Scholar
  11. 11.
    Karlgren, J. (ed.): New Text. Proceedings from the Workshop on New Text: Wikis and Blogs and Other Dynamic Text Sources, held in Conjunction with EACL. ACM, Trento (2006)Google Scholar
  12. 12.
    Karlgren, J.: The relation between author mood and affect to sentiment in text and text genre. In: ESAIR 2011, Fourth Workshop on Exploiting Semantic Annotation in Information Retrieval, Glasgow, Scotland (October 2011)Google Scholar
  13. 13.
    Kuppens, P., van Mechelen, I., Smits, D.J.M., de Boeck, P.: Associations between emotions: Correspondence across different types of data and componential basis. European Journal of Personality 18, 159–176 (2004)CrossRefGoogle Scholar
  14. 14.
    Mehrabian, A., Russell, J.A.: An approach to environmental psychology. M.I.T. Press, Cambridge (1974)Google Scholar
  15. 15.
    Mikels, J., Fredrickson, B., Larkin, G., Lindberg, C., Maglio, S.: Emotional category data on images from the International Affective Picture System. Behavior Research Methods, 626–630 (2005)Google Scholar
  16. 16.
    Morgan, R.L., Heise, D.: Structure of Emotions. Social Psychology Quarterly 51(1), 19–31 (1988)CrossRefGoogle Scholar
  17. 17.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundation and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  18. 18.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP 2002 (2002)Google Scholar
  19. 19.
    Sahlgren, M., Karlgren, J.: Terminology mining in social media. In: The 18th ACM Conference on Information and Knowledge Management (CIKM 2009), Hong Kong (November 2009)Google Scholar
  20. 20.
    Schumaker, R.P., Chen, H.: Evaluating a news-aware quantitative trader: The effects of momentum and contrarian stock selection strategies. Journal of the American Society for Information Science and Technology 59(2), 247–255 (2008)CrossRefGoogle Scholar
  21. 21.
    Schumaker, R.P., Chen, H.: A discrete stock price prediction engine based on financial news. Computer 43(1), 51–56 (2010)CrossRefGoogle Scholar
  22. 22.
    Schwarz, N.: Feelings as Information: Implications for Affective Influences on Information Processing. In: Martin, L., Clore, G. (eds.) Theories of Mood and Cognition. Lawrence Erlbaum, Mahwah (2001)Google Scholar
  23. 23.
    Seki, Y., Evans, D.K., Ku, L.W., Sun, L., Chen, H.H., Kando, N.: Overview of multilingual opinion analysis task at NTCIR-7. In: Proceedings of the 7th NTCIR Meeting. NII, Tokyo (2008)Google Scholar
  24. 24.
    Shih, C.C., Peng, T.C.: Building topic/trend detection system based on slow intelligence. In: DMS 2010 (2010)Google Scholar
  25. 25.
    Shmatov, K., Smirnov, M.: On some processes and distributions in a collective model of investors’ behavior. In: SSRN (2005), http://ssrn.com/abstract=739504
  26. 26.
    Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Oxford (1966)Google Scholar
  27. 27.
    Täckström, O., McDonald, R.: Discovering Fine-Grained Sentiment with Latent Variable Structured Prediction Models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 368–374. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  28. 28.
    Täckström, O., McDonald, R.: Semi-Supervised Fine-Grained Sentiment Analysis with Latent Variable Structured Conditional Models. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland (2011)Google Scholar
  29. 29.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39, 165–210 (2005)CrossRefGoogle Scholar
  30. 30.
    Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Social and Behavioral Sciences (2010)Google Scholar
  31. 31.
    Zhou, W.X., Sornette, D.: Renormalization group analysis of the 2000-2002 anti-bubble in the US S & P 500 index: Explanation of the hierarchy of 5 crashes and prediction. Physica A: Statistical Mechanics and its Applications 330, 584–604 (2003)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jussi Karlgren
    • 1
  • Magnus Sahlgren
    • 1
  • Fredrik Olsson
    • 1
  • Fredrik Espinoza
    • 1
  • Ola Hamfors
    • 1
  1. 1.GavagaiStockholmSweden

Personalised recommendations