Acquiring background knowledge to improve moral value prediction

Ying Lin, Joe Hoover, Gwenyth Portillo-Wightman, Christina Park, Morteza Dehghani, Heng Ji

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

Abstract

We address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are often only implicitly signaled in language, and tweets contain little contextual information due to length constraints. To address these obstacles, we present a novel approach to automatically acquire background knowledge from an external knowledge base to enrich input texts and thus improve moral value prediction. By combining basic textual features with background knowledge, our overall context-aware framework achieves performance comparable to a single human annotator. Our approach obtains 13.3% absolute F -score gains compared to our baseline model that only uses textual features. 1 1 Our code is available at https://github.com/limteng-rpi/mvp

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
EditorsAndrea Tagarelli, Chandan Reddy, Ulrik Brandes
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages552-559
Number of pages8
ISBN (Electronic)9781538660515
DOIs
StatePublished - Oct 24 2018
Externally publishedYes
Event10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 - Barcelona, Spain
Duration: Aug 28 2018Aug 31 2018

Publication series

NameProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018

Other

Other10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
CountrySpain
CityBarcelona
Period8/28/188/31/18

Keywords

  • Background Knowledge
  • Entity Linking
  • Moral Value Prediction
  • Natural Language Processing

ASJC Scopus subject areas

  • Sociology and Political Science
  • Communication
  • Computer Networks and Communications
  • Information Systems and Management

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