Sentiment Identification by Incorporating Syntax, Semantics and Context Information

被引：0

作者：

Zhang, Kunpeng ^{[1
]}

Xie, Yusheng ^{[1
]}

Cheng, Yu ^{[1
]}

Honbo, Daniel ^{[1
]}

Downey, Doug ^{[1
]}

Agrawal, Ankit ^{[1
]}

Liao, Wei-keng ^{[1
]}

Choudhary, Alok ^{[1
]}

机构：

[1] Northwestern Univ, EECS Dept, 2145 Sheridan Rd, Evanston, IL 60201 USA

来源：

SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2012年

关键词：

Sentiment; Syntax; Semantic; CRF; Active Learning;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1143 / 1144

页数：2

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