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      Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-annotated Tweets

      Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard
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      ijcai15.pdf
      Accepted version, 172.8Kb
      Link
       ijcai.org
      Citation
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      Bravo-Márquez, F., Frank, E., & Pfahringer, B. (2015). Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-annotated Tweets. In Q. Yang & M. Wooldridge (Eds.), Proc 24th International Joint Conference on Artificial Intelligence (pp. 1229–1235). Buenos Aires, Argentina: AAAI Press.
      Permanent Research Commons link: https://hdl.handle.net/10289/9630
      Abstract
      We present a supervised framework for expanding an opinion lexicon for tweets. The lexicon contains part-of-speech (POS) disambiguated entries with a three-dimensional probability distribution for positive, negative, and neutral polarities. To obtain this distribution using machine learning, we propose word-level attributes based on POS tags and information calculated from streams of emoticon annotated tweets. Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by semantic orientation, a state-of-the-art measure for establishing world-level sentiment.
      Date
      2015
      Type
      Conference Contribution
      Publisher
      AAAI Press
      Rights
      This is an author's accepted version of a paper published in the Proceedings of the 24th International Joint Conference on Artificial Intelligence. © 2015 International Joint Conferences on Artificial Intelligence.
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      • Computing and Mathematical Sciences Papers [1455]
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