Investigating the structure of emotions by analyzing similarity and association of emotion words
This work addresses the validation of a foundational emotion model in NLP, which is incremental as it tests an existing theory with new data.
This study investigated the validity of Plutchik's wheel of emotion model by analyzing semantic networks of emotion words based on similarity and association data, finding that the network structures were mostly similar but locally different from the wheel.
In the field of natural language processing, some studies have attempted sentiment analysis on text by handling emotions as explanatory or response variables. One of the most popular emotion models used in this context is the wheel of emotion proposed by Plutchik. This model schematizes human emotions in a circular structure, and represents them in two or three dimensions. However, the validity of Plutchik's wheel of emotion has not been sufficiently examined. This study investigated the validity of the wheel by creating and analyzing a semantic networks of emotion words. Through our experiments, we collected data of similarity and association of ordered pairs of emotion words, and constructed networks using these data. We then analyzed the structure of the networks through community detection, and compared it with that of the wheel of emotion. The results showed that each network's structure was, for the most part, similar to that of the wheel of emotion, but locally different.