Sentiment identification by incorporating syntax, semantics and context information

Authors:
Kunpeng Zhang;Yusheng Xie;Yu Cheng;Daniel Honbo;Doug Downey;Ankit Agrawal;Wei-keng Liao;Alok Choudhary
Affiliations:
Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA;Northwestern university, EVANSTON, IL, USA
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

Citing 2
Cited 0

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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning with compositional semantics as structural inference for subsentential sentiment analysis

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing

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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.