Prastyo, Pulung Hendro and Ardiyanto, Igi and Hidayat, Risanuri (2021) A Combination of Query Expansion Ranking and GA-SVM for Improving Indonesian Sentiment Classification Performance. In: Procedia Computer Science.
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The sentiment classification method is a research field that is proliferating in Indonesia since it is fast in extracting public opinion and provides essential and valuable information for stakeholders. Of the best-performing sentiment classification approaches, machine learning is one of them that has excellent performance. However, the method has several problems, such as noisy features and high dimensionality of features that significantly affect the sentiment classification performance. Therefore, to overcome the problems, this paper presents a novel feature selection using a combination of Query Expansion Ranking (QER) and Genetic Algorithm-Support Vector Machine (GA-SVM) for improving sentiment classification performance. Based on the experimental results, the proposed method could significantly improve sentiment classification performance, outperform all state-of-the-art algorithms, and decrease computational time. The method achieved the best performance in average precision, recall, and f-measure with the value of 96.78, 96.76, and 96.75, respectively. © 2021 Elsevier B.V.. All rights reserved.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 3; All Open Access, Gold Open Access |
Uncontrolled Keywords: | Classification (of information); Feature extraction; Social aspects; Support vector machines; Classification methods; Classification performance; Elsevier; Features selection; Genetic algorithm support vector machines; Machine-learning; Performance; Query expansion; Query expansion ranking; Sentiment classification; Genetic algorithms |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 06 Oct 2024 11:17 |
Last Modified: | 06 Oct 2024 11:17 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8755 |