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

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.