Extracting and Clustering of Story Events from a Story Corpus

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 cl...

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Bibliographic Details
Published in:KSII Transactions on Internet and Information Systems 2021, Vol.15 (10), p.3498
Main Authors: Yu, Hye-Yeon, Cheong, Yun-Gyung, Bae, Byung-Chull
Format: Report
Language:English
Subjects:
Computational linguistics
Language processing
Methods
Natural language interfaces
Storytelling
Technology application
Online Access:Get full text
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Summary: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. Keywords: Event Representation, Emotional Event, Event Clustering, Event Extraction, Sentiment Analysis of Story Event.
ISSN:1976-7277
1976-7277
DOI:10.3837/tiis.2021.10.002