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
Country/TerritoryUnited States
CityPortland, OR
Period8/12/128/16/12

Keywords

  • active learning
  • crf
  • semantic
  • sentiment
  • syntax

ASJC Scopus subject areas

  • Information Systems

Fingerprint

Dive into the research topics of 'Sentiment identification by incorporating syntax, semantics and context information'. Together they form a unique fingerprint.

Cite this