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.
CITATION STYLE
Yu, H. Y., Park, S., Cheong, Y. G., Kim, M. H., & Bae, B. C. (2019). Emotion-based story event clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11869 LNCS, pp. 348–353). Springer. https://doi.org/10.1007/978-3-030-33894-7_36
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