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Umigon-lexicon: rule-based model for interpretable sentiment analysis and factuality categorization

Author

Listed:
  • Clement Levallois

    (EM - EMLyon Business School)

Abstract

We introduce umigon-lexicon, a novel resource comprising English lexicons and associated conditions designed specifically to evaluate the sentiment conveyed by an author's subjective perspective. We conduct a comprehensive comparison with existing lexicons and evaluate umigon-lexicon's efficacy in sentiment analysis and factuality classification tasks. This evaluation is performed across eight datasets and against six models. The results demonstrate umigon-lexicon's competitive performance, underscoring the enduring value of lexicon-based solutions in sentiment analysis and factuality categorization. Furthermore, umigon-lexicon stands out for its intrinsic interpretability and the ability to make its operations fully transparent to end users, offering significant advantages over existing models.

Suggested Citation

  • Clement Levallois, 2024. "Umigon-lexicon: rule-based model for interpretable sentiment analysis and factuality categorization," Post-Print hal-04615116, HAL.
    • Handle: RePEc:hal:journl:hal-04615116
    DOI: 10.1007/s10579-024-09742-y
    as

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