Emotion-based story event clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationInteractive Storytelling - 12th International Conference on Interactive Digital Storytelling, ICIDS 2019, Proceedings
EditorsRogelio E. Cardona-Rivera, R. Michael Young, Anne Sullivan
PublisherSpringer
Pages348-353
Number of pages6
ISBN (Print)9783030338930
DOIs
StatePublished - 2019
Event12th International Conference on Interactive Digital Storytelling, ICIDS 2019 - Salt Lake City, United States
Duration: 19 Nov 201922 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11869 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Interactive Digital Storytelling, ICIDS 2019
Country/TerritoryUnited States
CitySalt Lake City
Period19/11/1922/11/19

Keywords

  • Event clustering
  • Event extraction
  • Event representation

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