Proceedings of the Annual Meeting of the Cognitive Science Society

There is growing interest in emotions in textual data. Based on psychological theories, research has been conducted on assigning emotions as labels to text datasets or developing models to detect emotions present in text. However, little research has been done on the appropriateness of using these theories. In this study, we reviewed three commonly used basic emotion theories: Ekman’s Basic Emotions, Plutchik’s Wheel of Emotions, and GoEmotions. By leveraging a research finding that evaluated the emotional values of words, we were able to fine-tune a language model emotionally. Using it, we analyzed the emotional relationship between the names of basic emotions and evaluated the adequacy of the emotional structure each theory presents. Clear patterns of similarity emerged based on the emotional meaning of the words. Ekman’s and GoEmotions were almost in line with our results, while Plutchik’s had some differences. We discussed these matches and mismatches.