Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights

Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper pr...

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Bibliographic Details
Published inICT express Vol. 8; no. 2; pp. 189 - 197
Main Authors Prastyo, Pulung Hendro, Hidayat, Risanuri, Ardiyanto, Igi
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2022
Elsevier
한국통신학회
Subjects
Binary Particle Swarm Optimization
Feature selection
Machine learning
Query Expansion Ranking
Sentiment classification
전자/정보통신공학
Feature selection
Query Expansion Ranking
Binary Particle Swarm Optimization
Sentiment classification
Machine learning
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Summary:Machine learning-based sentiment classification is the best-performing method to understand public sentiment. However, the method has some problems, such as noisy features and high-dimensional feature space which affect the sentiment classification performance. To address the problems, this paper proposes a new feature selection using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights. The proposed method was validated using five tweet datasets on different topics both in Indonesian and English, and compared with state-of-the-art of filter and wrapper-based feature selection methods. Experimental results show the proposed method significantly improves sentiment classification performance and decrease computational time.
ISSN:2405-9595
2405-9595
DOI:10.1016/j.icte.2021.04.009