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Emotion-Corpus Guided Lexicons for Sentiment Analysis on Twitter

Abstract

Conceptual frameworks for emotion to sentiment mapping have been proposed in Psychology research. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn word-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology (Cambria et al. 28th AAAI Conference on Artificial Intelligence, pp. 1515–1521, 2014) [1] for automated generation of sentiment lexicons. Sentiment analysis experiments on benchmark Twitter data sets confirm the quality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentiment classification and sentiment intensity prediction tasks.

Keywords

  • Emotion
  • Sentiment
  • Domain-specific Lexicons
  • Expectation Maximization
  • Emotion Theories
  • Twitter

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Fig. 1
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Notes

  1. 1.

    https://dev.twitter.com/streaming/public.

  2. 2.

    http://www.gabormelli.com/RKB/Distant-Supervision-Learning-Algorithm.

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Correspondence to Anil Bandhakavi , Nirmalie Wiratunga , Stewart Massie or P. Deepak .

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Bandhakavi, A., Wiratunga, N., Massie, S., Deepak, P. (2016). Emotion-Corpus Guided Lexicons for Sentiment Analysis on Twitter. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-47175-4_5

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