Emotion-Based Story Event Clustering

Hye-Yeon Yu, Seohui Park, Yun-Gyung Cheong, Moon-Hyun Kim, Byung-Chull Bae

Abstract: In this paper we explore how events can be represented and extracted from text stories, and describe the results from our simple experiment on extracting and clustering events. We applied k-means clustering algorithm and NLTK-VADER sentiment analyzer based on Plutchik’s 8 basic emotion model. When compared with human raters, some emotions show low accuracy while other emotion types, such as joy and sadness, show relatively high accuracy using our method.