Unsupervised sentiment-bearing feature selection for document-level sentiment classification

Yan Li, Zhen Qin, Weiran Xu, Heng Ji, Jun Guo

Research output: Contribution to journalArticlepeer-review

Abstract

Text sentiment classification aims to automatically clas-sify subjective documents into different sentiment-oriented categories (e.g. positive/negative). Given the high dimensionality of features describing documents, how to effectively select the most useful ones, referred to as sentiment-bearing features, with a lack of sentiment class labels is crucial for improving the classification performance. This paper proposes an unsu-pervised sentiment-bearing feature selection method (USFS), which incor-porates sentiment discriminant analysis (SDA) into sentiment strength cal-culation (SSC). SDA applies traditional linear discriminant analysis (LDA) in an unsupervised manner without losing local sentiment information be-tween documents. We use SSC to calculate the overall sentiment strength for each single feature based on its affinities with some sentiment priors. Experiments, performed using benchmark movie reviews, demonstrated the superior performance of USFS.

Original languageEnglish (US)
Pages (from-to)2805-2813
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE96-D
Issue number12
DOIs
StatePublished - Dec 2013
Externally publishedYes

Keywords

  • Feature selection
  • Sentiment classification
  • Sentiment discriminant analysis
  • Sentiment strength calculation

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Unsupervised sentiment-bearing feature selection for document-level sentiment classification'. Together they form a unique fingerprint.

Cite this