• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Extracting and Clustering of Story Events from a Story Corpus


Abstract

This article describes how events that make up text stories can be represented and extracted. We also address the results from our simple experiment on extracting and clustering events in terms of emotions, under the assumption that different emotional events can be associated with the classified clusters. Each emotion cluster is based on Plutchik’s eight basic emotion model, and the attributes of the NLTK-VADER are used for the classification criterion. While comparisons of the results with human raters show less accuracy for certain emotion types, emotion types such as joy and sadness show relatively high accuracy. The evaluation results with NRC Word Emotion Association Lexicon (aka EmoLex) show high accuracy values (more than 90% accuracy in anger, disgust, fear, and surprise), though precision and recall values are relatively low.


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Cite this article

[IEEE Style]
H. Yu, Y. Cheong and B. Bae, "Extracting and Clustering of Story Events from a Story Corpus," KSII Transactions on Internet and Information Systems, vol. 15, no. 10, pp. 3498-3512, 2021. DOI: 10.3837/tiis.2021.10.002.

[ACM Style]
Hye-Yeon Yu, Yun-Gyung Cheong, and Byung-Chull Bae. 2021. Extracting and Clustering of Story Events from a Story Corpus. KSII Transactions on Internet and Information Systems, 15, 10, (2021), 3498-3512. DOI: 10.3837/tiis.2021.10.002.

[BibTeX Style]
@article{tiis:25010, title="Extracting and Clustering of Story Events from a Story Corpus", author="Hye-Yeon Yu and Yun-Gyung Cheong and Byung-Chull Bae and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2021.10.002}, volume={15}, number={10}, year="2021", month={October}, pages={3498-3512}}