Learning Sentiments from Tweets with Personal Health Information

  • Victoria Bobicev
  • Marina Sokolova
  • Yasser Jafer
  • David Schramm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)


We present results of sentiment analysis in Twitter messages that disclose personal health information. In these messages (tweets), users discuss ailment, treatment, medications, etc. We use the author-centric annotation model to label tweets as positive sentiments, negative sentiments or neutral. The results of the agreement among three raters are reported and discussed. We then use Machine Learning methods on multi-class and binary classification of sentiments. The obtained results are comparable with previous results in the subjectivity analysis of user-written Web content.


sentiment analysis personal health information Twitter 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allan, K.: Explorations in Classical Sociological Theory: Seeing the Social World. Pine Forge Press (2005)Google Scholar
  2. 2.
    Balahur, A., Steinberger, R.: Rethinking Sentiment Analysis in the News: from Theory to Practice and back. In: Proceedings of the 1st Workshop on Opionion Mining and Sentiment Analysis (2009)Google Scholar
  3. 3.
    Chen, W.: Dimensions of Subjectivity in Natural Language (Short Paper). In: Proceedings of ACL-HLT (2008)Google Scholar
  4. 4.
    Chew, C., Eysenbach, G.: Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS One 5(11) (2010)Google Scholar
  5. 5.
    Dodds, P., Harris, K., Kloumann, I., Bliss, C., Danforth, C.: Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 6, e26752 (2011)Google Scholar
  6. 6.
    Green, A.: Kappa statistics for multiple raters using categorical classifications. In: Proceedings of the 22nd Annual Conference of SAS Users Group (1997)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Lampos, V., Christianini, N.: Tracking the flu pandemic by monitoring the social web. In: 2nd Workshop on Cognitive Information Processing (2010)Google Scholar
  9. 9.
    Nichols, T., Wisner, P., Cripe, G., Gulabchand, L.: Putting the Kappa Statistic to Use. Qual. Assur. Journal 13, 57–61 (2010)CrossRefGoogle Scholar
  10. 10.
    O’Connor, B., Balasubramanyan, R., Routledge, B., Smith, N.: From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 122–129 (2010)Google Scholar
  11. 11.
    Osman, D., Yearwood, J., Vamplew, P.: Automated opinion detection: Implications of the level of agreement between human raters. Information Processing and Management 46, 331–342 (2010)CrossRefGoogle Scholar
  12. 12.
    Pennebaker, J., Chung, C.: Expressive Writing, Emotional Upheavals, and Health. In: Friedman, H., Silver, R. (eds.) Handbook of Health Psychology. Oxford University Press (2006)Google Scholar
  13. 13.
    Sokolova, M., Lapalme, G.: Learning opinions in user-generated Web content. Journal of Natural Language Engineering (2011)Google Scholar
  14. 14.
    Sokolova, M., Schramm, D.: Building a patient-based ontology for mining user-written content. In: Recent Advances in Natural Language Processing, pp. 758–763 (2011)Google Scholar
  15. 15.
    Sokolova, M., Bobicev, V.: Sentiments and Opinions in Health-related Web messages. In: Recent Advances in Natural Language Processing, pp. 132–139 (2011)Google Scholar
  16. 16.
    Strapparava, C., Mihalcea, R.: Learning to Identify Emotions in Text. In: Proceedings of the 2008 ACM Symposium on Applied Computing (2008)Google Scholar
  17. 17.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in Twitter events. Journal of the American Society for Information Science and Technology 62(2), 406–418 (2010)CrossRefGoogle Scholar
  18. 18.
    Thelwall, M., Wilkinson, D., Uppal, S.: Data Mining Emotion in Social Network Communication: Gender Differences in MySpace. Journal of the American Society for Information Science and Technology 61(1), 190–199 (2010)CrossRefGoogle Scholar
  19. 19.
    Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufman (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Victoria Bobicev
    • 1
  • Marina Sokolova
    • 2
    • 3
    • 4
  • Yasser Jafer
    • 3
  • David Schramm
    • 4
    • 5
  1. 1.Department of Applied InformaticsTechnical University of MoldovaMoldova
  2. 2.Electronic Health Information LabCHEO Research InstituteCanada
  3. 3.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  4. 4.Faculty of MedicineUniversity of OttawaOttawaCanada
  5. 5.Children’s Hospital of Eastern OntarioCanada

Personalised recommendations