Sentiment and Factual Transitions in Online Medical Forums

  • Victoria Bobicev
  • Marina SokolovaEmail author
  • Michael Oakes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9091)


This work studies sentiment and factual transitions on an online medical forum where users correspond in English. We work with discussions dedicated to reproductive technologies, an emotionally-charged issue. In several learning problems, we demonstrate that multi-class sentiment classification significantly improves when messages are represented by affective terms combined with sentiment and factual transition information (paired t-test, P=0.0011).


Irritable Bowel Syndrome Factual Transition Sentiment Analysis Factual Category Sentiment Category 
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.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Victoria Bobicev
    • 1
  • Marina Sokolova
    • 2
    • 3
    Email author
  • Michael Oakes
    • 4
  1. 1.Technical University of MoldovaChisinauMoldova
  2. 2.Institute for Big Data AnalyticsHalifaxCanada
  3. 3.Faculty of Medicine and Faculty of EngineeringUniversity of OttawaOttawaCanada
  4. 4.Research Group in Computational LinguisticsUniversity of WolverhamptonWolverhamptonUK

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