A word-scale probabilistic latent variable model for detecting human values

Yasuhiro Takayama, Yoichi Tomiura, Emi Ishita, Douglas W. Oard, Kenneth R. Fleischmann, An Shou Cheng

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

4 Citations (Scopus)

Abstract

This paper describes a probabilistic latent variable model that is designed to detect human values such as justice or freedom that a writer has sought to reflect or appeal to when participating in a public debate. The proposed model treats the words in a sentence as having been chosen based on specific values; values reflected by each sentence are then estimated by aggregating values associated with each word. The model can determine the human values for the word in light of the influence of the previous word. This design choice was motivated by syntactic structures such as noun+noun, adjective+noun, and verb+adjective. The classifier based on the model was evaluated on a test collection containing 102 manually annotated documents focusing on one contentious political issue - Net neutrality, achieving the highest reported classification effectiveness for this task. We also compared our proposed classifier with human second anno-tator. As a result, the proposed classifier effectiveness is statistically comparable with human annotators.

Original languageEnglish
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages1489-1498
Number of pages10
ISBN (Electronic)9781450325981
DOIs
Publication statusPublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Publication series

NameCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

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