Sentiment identification by incorporating syntax, semantics and context information

Kunpeng Zhang*, Yusheng Xie, Yu Cheng, Daniel Honbo, Douglas C Downey, Ankit Agrawal, Wei-Keng Liao, Alok Nidhi Choudhary

*Corresponding author for this work

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

6 Scopus citations

Abstract

This paper proposes a method based on conditional random fields to incorporate sentence structure (syntax and semantics) and context information to identify sentiments of sentences within a document. It also proposes and evaluates two different active learning strategies for labeling sentiment data. The experiments with the proposed approach demonstrate a 5-15% improvement in accuracy on Amazon customer reviews compared to existing supervised learning and rule-based methods.

Original languageEnglish (US)
Title of host publicationSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1143-1144
Number of pages2
DOIs
StatePublished - Sep 28 2012
Event35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 - Portland, OR, United States
Duration: Aug 12 2012Aug 16 2012

Publication series

NameSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
CountryUnited States
CityPortland, OR
Period8/12/128/16/12

Keywords

  • active learning
  • crf
  • semantic
  • sentiment
  • syntax

ASJC Scopus subject areas

  • Information Systems

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