An Ensemble Framework for Dynamic Character Relationship Sentiment in Fiction

Nikolaus Nova Parulian, Glen Worthey, J Stephen Downie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fictional characters in a narrative text can experience various events in the narrative timeline as the progress of character development. Relationships between characters can also dynamically change over time. Summarizing the relationship dynamics in fiction through manual annotation can be very tedious even at a small scale, but highly impractical or even impossible in a large corpus. With the recent development of machine learning models in Natural Language Processing, many tasks have been introduced to help humans extract information from text automatically. Motivated by this development, we propose a conceptual model and an information extraction framework that combines two state-of-the-art machine learning algorithms to extract character relationships directly from an event sentence in a fictional narrative. For our use case, as we consider sequence in a story line, we also infer the dynamic sentiment relationships among characters over time. Since this approach is by nature unsupervised, we also preserve the provenance of each relation extracted in order to prepare a dataset to use in training a supervised model. We hope this approach can be a step toward more robust automatic character relation and event extraction from fictional texts.

Original languageEnglish (US)
Title of host publicationInformation for a Better World
Subtitle of host publicationShaping the Global Future - 17th International Conference, iConference 2022, Proceedings
EditorsMalte Smits
PublisherSpringer
Pages414-424
Number of pages11
ISBN (Print)9783030969561
DOIs
StatePublished - 2022
Event17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022 - Virtual, Online
Duration: Feb 28 2022Mar 4 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13192 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Information for a Better World: Shaping the Global Future, iConference 2022
CityVirtual, Online
Period2/28/223/4/22

Keywords

  • Digital library
  • Information extraction
  • Machine learning
  • Network analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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