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Contextual Sentiment Analysis in Social Media Using High-Coverage Lexicon

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Research and Development in Intelligent Systems XXX (SGAI 2013)

Abstract

Automatically generated sentiment lexicons offer sentiment information for a large number of terms and often at a more granular level than manually generated ones. While such rich information has the potential of enhancing sentiment analysis, it also presents the challenge of finding the best possible strategy to utilising the information. In SentiWordNet, negation terms and lexical valence shifters (i.e. intensifier and diminisher terms) are associated with sentiment scores. Therefore, such terms could either be treated as sentiment-bearing using the scores offered by the lexicon, or as sentiment modifiers that influence the scores assigned to adjacent terms. In this paper, we investigate the suitability of both these approaches applied to sentiment classification. Further, we explore the role of non-lexical modifiers common to social media and introduce a sentiment score aggregation strategy named SmartSA. Evaluation on three social media datasets show that the strategy is effective and outperform the baseline of using aggregate-and-average approach.

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Notes

  1. 1.

    http://nlp.stanford.edu/software/corenlp.shtml

  2. 2.

    http://www.ark.cs.cmu.edu/TweetNLP

  3. 3.

    The dataset are obtained from the cyberemotions project: http://www.cyberemotions.eu

  4. 4.

    Available from http://www.sentiment140.com

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Authors and Affiliations

  1. IDEAS Research Institute, Robert Gordon University, Aberdeen, Scotland

    Aminu Muhammad, Nirmalie Wiratunga, Robert Lothian & Richard Glassey

Authors
  1. Aminu Muhammad
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  2. Nirmalie Wiratunga
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  3. Robert Lothian
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  4. Richard Glassey
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Correspondence to Aminu Muhammad .

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Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, West Sussex, United Kingdom

    Miltos Petridis

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Muhammad, A., Wiratunga, N., Lothian, R., Glassey, R. (2013). Contextual Sentiment Analysis in Social Media Using High-Coverage Lexicon. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-02621-3_6

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