Selecting attributes for sentiment classification using feature relation networks

Ahmed Abbasi, Stephen France, Zhu Zhang, Hsinchun Chen

Research output: Contribution to journalArticle

116 Scopus citations

Abstract

A major concern when incorporating large sets of diverse n-gram features for sentiment classification is the presence of noisy, irrelevant, and redundant attributes. These concerns can often make it difficult to harness the augmented discriminatory potential of extended feature sets. We propose a rule-based multivariate text feature selection method called Feature Relation Network (FRN) that considers semantic information and also leverages the syntactic relationships between n-gram features. FRN is intended to efficiently enable the inclusion of extended sets of heterogeneous n-gram features for enhanced sentiment classification. Experiments were conducted on three online review testbeds in comparison with methods used in prior sentiment classification research. FRN outperformed the comparison univariate, multivariate, and hybrid feature selection methods; it was able to select attributes resulting in significantly better classification accuracy irrespective of the feature subset sizes. Furthermore, by incorporating syntactic information about n-gram relations, FRN is able to select features in a more computationally efficient manner than many multivariate and hybrid techniques.

Original languageEnglish (US)
Article number5510238
Pages (from-to)447-462
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume23
Issue number3
DOIs
StatePublished - Jan 31 2011

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Keywords

  • Natural language processing
  • affective computing
  • machine learning
  • subspace selection
  • text mining

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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