A review of feature selection techniques in sentiment analysis

Research output: Contribution to journalReview article

Abstract

The rapid growth in web development has transformed today's communication. The combination of features and corresponding sentiment words (SWs) can help produce accurate, meaningful, and high-quality sentiment analysis (SA) results. There are some basic matters in the study of SA that must be understood, namely, the objects or entities that form a key part of the discussion, the characteristics or features of the object, the SWs, and the connection between the features of the object and the SWs. Failure to identify these basic matters can reduce the accuracy and meaning of the SA results. The main objective of this review is to offer an overview of the role and techniques of feature selection (FS), SWs detection, and the identification of the relationship between features and SWs. The main contributions of this review are its sophisticated categorisations of a large number of recent articles related to FS techniques and the detection of SWs. It also highlights the recent trends in the field of SA research. This review will also look at the metaheuristic approach as a FS technique in SA, identify the strengths and weaknesses of existing FS techniques, and analyse the potential of the metaheuristic approach for solving problems that exist in the selection of features in SA.

Original languageEnglish
Pages (from-to)159-189
Number of pages31
JournalIntelligent Data Analysis
Volume23
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Sentiment Analysis
Feature Selection
Feature extraction
Metaheuristics
Categorization
Review
Communication
Object

Keywords

  • ant colony optimization
  • feature selection
  • metaheuristic algorithm
  • Sentiment analysis
  • sentiment word

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

A review of feature selection techniques in sentiment analysis. / Ahmad, Siti Rohaidah; Abu Bakar, Azuraliza; Yaakub, Mohd Ridzwan.

In: Intelligent Data Analysis, Vol. 23, No. 1, 01.01.2019, p. 159-189.

Research output: Contribution to journalReview article

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