TONE: A 3-Tiered ONtology for Emotion Analysis

Srishti Gupta, Piyush Kumar Garg, Sourav Kumar Dandapat

Abstract: Emotion ontologies facilitate better communication, support mental health interventions, and advance research in fields such as psychology, artificial intelligence, and social sciences. Existing emotion ontologies often fall short of capturing the complexity and fluidity of human emotions, limiting their effectiveness in varied fields. Major issues include the oversimplification of emotions into rigid categories, inadequate representation of emotional transitions, and cultural biases hindering global applicability. Models like Ekman’s basic emotions or Plutchik’s wheel provide valuable insights but fail to account for the nuanced relationships between emotions, such as how one emotion can trigger or intensify another. To address these limitations, this paper proposes an emotion ontology, TONE, based on Gerrod Parrott’s hierarchical classification of emotions. This classification allows for greater granularity, capturing subtle variations and acknowledging the interrelatedness of emotions using various dependencies. By leveraging Parrott’s model, TONE offers a flexible, culturally adaptable framework that more accurately reflects the dynamic nature of emotional experiences. This ontology is available at https://github.com/srishtigupta253/TONE.git.