Sentiment Analysis for User Generated Discourse in eLearning 2.0


One prominent feature of eLearning 2.0 is collaboration. Members interact, learn
and share their opinions by creating mass amount of discourse through wikis, blogs and
forums. However, this growing amount of user generated discourse places considerable
burdens on learners as well as instructors who wish to track learners' opinions and views
on diverse topics or search for content containing opinions.


Enabling subjectivity and sentiment analysis for generating feedback from user generated discourse and for supporting information search in eLearning 2.0:

  • investigate knowledge- and corpus-based methods for subjectivity and sentiment analysis
  • determine the semantic orientation and strength of the opinions
  • identify the targets of the opinions
  • identify the holders of the opinions

System Architecture

Project Publications

Additional Attributes


Sentence and Expression Level Annotation of Opinions in User-Generated Discourse

Cigdem Toprak, Niklas Jakob, Iryna Gurevych
In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), p. 575 -- 584, July 2010
Association for Computational Linguistics

Document Level Subjectivity Classification Experiments in DEFT'09 Challenge

Cigdem Toprak, Iryna Gurevych
In: Proceedings of the DEFT'09 Text Mining Challenge, p. 89-97, June 2009


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