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.
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Acknowledgments
This work was supported by Institute for Information communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-01772, Development of QA systems for Video Story Understanding to pass the Video Turing Test) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2017R1A2B4010499). This research was also supported by MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01642) supervised by the IITP (Institute for Information communications Technology Promotion).
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Yu, HY., Park, S., Cheong, YG., Kim, MH., Bae, BC. (2019). Emotion-Based Story Event Clustering. In: Cardona-Rivera, R., Sullivan, A., Young, R. (eds) Interactive Storytelling. ICIDS 2019. Lecture Notes in Computer Science(), vol 11869. Springer, Cham. https://doi.org/10.1007/978-3-030-33894-7_36
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DOI: https://doi.org/10.1007/978-3-030-33894-7_36
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