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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)

Abstract

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.

Keywords

sentiment analysis personal health information Twitter 

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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

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