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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959521000539 |
| _version_ | 1818232548218634240 |
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| author | Pulung Hendro Prastyo Risanuri Hidayat Igi Ardiyanto |
| author_facet | Pulung Hendro Prastyo Risanuri Hidayat Igi Ardiyanto |
| author_sort | Pulung Hendro Prastyo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T11:08:02Z |
| format | Article |
| id | doaj.art-305a64b6e348458da64f09c576a13fc7 |
| institution | Directory Open Access Journal |
| issn | 2405-9595 |
| language | English |
| last_indexed | 2024-12-12T11:08:02Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj.art-305a64b6e348458da64f09c576a13fc72022-12-22T00:26:22ZengElsevierICT Express2405-95952022-06-0182189197Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia WeightsPulung Hendro Prastyo0Risanuri Hidayat1Igi Ardiyanto2Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaDepartment of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaCorresponding author.; Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, IndonesiaMachine 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.http://www.sciencedirect.com/science/article/pii/S2405959521000539Feature selectionSentiment classificationMachine learningQuery Expansion RankingBinary Particle Swarm Optimization |
| spellingShingle | Pulung Hendro Prastyo Risanuri Hidayat Igi Ardiyanto Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights ICT Express Feature selection Sentiment classification Machine learning Query Expansion Ranking Binary Particle Swarm Optimization |
| title | Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights |
| title_full | Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights |
| title_fullStr | Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights |
| title_full_unstemmed | Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights |
| title_short | Enhancing sentiment classification performance using hybrid Query Expansion Ranking and Binary Particle Swarm Optimization with Adaptive Inertia Weights |
| title_sort | enhancing sentiment classification performance using hybrid query expansion ranking and binary particle swarm optimization with adaptive inertia weights |
| topic | Feature selection Sentiment classification Machine learning Query Expansion Ranking Binary Particle Swarm Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2405959521000539 |
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