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A comprehensive study of domain-specific emoji meanings in sentiment classification

Author

Listed:
  • Nader Mahmoudi

    (University of Newcastle)

  • Łukasz P. Olech

    (Wroclaw University of Science and Technology)

  • Paul Docherty

    (Monash University)

Abstract

The inclusion of emojis when solving natural language processing problems (e.g., text‐based emotion detection, sentiment classification, topic analysis) improves the quality of the results. However, the existing literature focuses only on the general meaning transferred by emojis and has not examined emojis in the context of investor sentiment classification. This article provides a comprehensive study of the impact that inclusion of emojis could make in predicting stock investors’ sentiment. We found that a classifier that incorporates domain-specific emoji vectors, which capture the syntax and semantics of emojis in the financial context, could improve the accuracy of investor sentiment classification. Also, when domain-specific emoji vectors are considered, daily time-series of investor sentiment demonstrated additional marginal explanatory power on returns and volatility. Further, a comparison of conducted cluster analysis of domain-specific versus domain-independent emoji vectors showed different natural groupings of emojis reflecting domain specificity when special meaning of emojis is considered. Finally, domain-specific emoji vectors could result in the development of significantly superior emoji sentiment lexicons. Given the importance of domain-specific emojis in investor sentiment classification of social media data, we have developed an emoji lexicon that could be used by other researchers.

Suggested Citation

  • Nader Mahmoudi & Łukasz P. Olech & Paul Docherty, 2022. "A comprehensive study of domain-specific emoji meanings in sentiment classification," Computational Management Science, Springer, vol. 19(2), pages 159-197, June.
    • Handle: RePEc:spr:comgts:v:19:y:2022:i:2:d:10.1007_s10287-021-00407-7
    DOI: 10.1007/s10287-021-00407-7
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