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      Determining word–emotion associations from tweets by multi-label classification

      Bravo-Marquez, Felipe; Frank, Eibe; Mohammad, Saif M.; Pfahringer, Bernhard
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      Accepted-version-emo_lex_wi.pdf
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      DOI
       10.1109/WI.2016.90
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      Bravo-Marquez, F., Frank, E., Mohammad, S. M., & Pfahringer, B. (2016). Determining word–emotion associations from tweets by multi-label classification. In Proceedings of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, USA, 13-16 October, 2016(pp. 536–539). Los Alamitos, CA, USA: IEEE Computer Society. http://doi.org/10.1109/WI.2016.90
      Permanent Research Commons link: https://hdl.handle.net/10289/10783
      Abstract
      The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter. We perform this expansion using multi-label classification of words and compare different wordlevel features extracted from unlabelled tweets such as unigrams, Brown clusters, POS tags, and word2vec embeddings. The results show that the expanded lexicon achieves major improvements over the original lexicon when classifying tweets into emotional categories. In contrast to previous work, our methodology does not depend on tweets annotated with emotional hashtags, thus enabling the identification of emotional words from any domainspecific collection using unlabelled tweets.
      Date
      2016
      Type
      Conference Contribution
      Publisher
      IEEE Computer Society
      Rights
      This is an author’s accepted version of an article published in the Proceeding of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence. © 2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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      • Computing and Mathematical Sciences Papers [1445]
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