Scalable sentiment classification across multiple dark web forums

David Zimbra, Hsinchun Chen

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

9 Scopus citations

Abstract

This study examines several approaches to sentiment classification in the Dark Web Forum Portal, and opportunities to transfer classifiers and text features across multiple forums to improve scalability and performance. Although sentiment classifiers typically perform poorly when transferred across domains, experimentation reveals the devised approaches offer performance equivalent to the traditional forum-specific approach in classification in an unknown domain. Furthermore, incorporating the text features identified as significant indicators of sentiment in other forums can greatly improve the classification accuracy of the traditional forum-specific approach.

Original languageEnglish (US)
Title of host publicationISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publicationCyberspace, Border, and Immigration Securities
Pages78-83
Number of pages6
DOIs
StatePublished - 2012
Event2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012 - Washington, DC, United States
Duration: Jun 11 2012Jun 14 2012

Publication series

NameISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics: Cyberspace, Border, and Immigration Securities

Other

Other2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012
Country/TerritoryUnited States
CityWashington, DC
Period6/11/126/14/12

Keywords

  • dark web
  • domain transfer
  • sentiment analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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