A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews
Feature extraction and selection are critical in sentiment analysis (SA) to extract and select only the appropriate features by removing those deemed redundant. As such, the successful implementation of this process leads to better classification accuracy. Inevitably, selecting high-quality minimal...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2022
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| Online Access: | https://repo.uum.edu.my/id/eprint/29111/1/JICT%2021%2004%202022%20571-593.pdf https://doi.org/10.32890/jict2022.21.4.5 |
| _version_ | 1825805924223156224 |
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| author | Mat Zin, Harnani Mustapha, Norwati Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina |
| author_facet | Mat Zin, Harnani Mustapha, Norwati Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina |
| author_sort | Mat Zin, Harnani |
| collection | UUM |
| description | Feature extraction and selection are critical in sentiment analysis (SA) to extract and select only the appropriate features by removing those deemed redundant. As such, the successful implementation of this process leads to better classification accuracy. Inevitably, selecting high-quality minimal features can be challenging given the inherent complication in dealing with over-fitting issues. Most of the current studies used a heuristic method to perform the classification process that will result in selecting and examining only a single feature subset, while ignoring the other subsets that might give better results. This study explored the effect of using the meta-heuristic method together with the ensemble classification method in the sentiment classification of online reviews. Adding to that point, the extraction and selection of relevant features used feature ranking, hyper-parameter optimization, crossover, and mutation, while the classification process utilized the ensemble classifier. The proposed method was tested on the polarity movie review dataset v2.0 and product review dataset (books, electronics, kitchen, and music). The test results indicated that the proposed method significantly improved the classification results by 94%, which far exceeded the existing method. Therefore, the proposed feature extraction and selection method can help in improving the performance of SA in online reviews and, at the same time, reduce the number of extracted features. |
| first_indexed | 2024-07-04T06:40:11Z |
| format | Article |
| id | uum-29111 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:40:11Z |
| publishDate | 2022 |
| publisher | Universiti Utara Malaysia Press |
| record_format | eprints |
| spelling | uum-291112023-02-09T03:13:33Z https://repo.uum.edu.my/id/eprint/29111/ A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews Mat Zin, Harnani Mustapha, Norwati Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina QA75 Electronic computers. Computer science Feature extraction and selection are critical in sentiment analysis (SA) to extract and select only the appropriate features by removing those deemed redundant. As such, the successful implementation of this process leads to better classification accuracy. Inevitably, selecting high-quality minimal features can be challenging given the inherent complication in dealing with over-fitting issues. Most of the current studies used a heuristic method to perform the classification process that will result in selecting and examining only a single feature subset, while ignoring the other subsets that might give better results. This study explored the effect of using the meta-heuristic method together with the ensemble classification method in the sentiment classification of online reviews. Adding to that point, the extraction and selection of relevant features used feature ranking, hyper-parameter optimization, crossover, and mutation, while the classification process utilized the ensemble classifier. The proposed method was tested on the polarity movie review dataset v2.0 and product review dataset (books, electronics, kitchen, and music). The test results indicated that the proposed method significantly improved the classification results by 94%, which far exceeded the existing method. Therefore, the proposed feature extraction and selection method can help in improving the performance of SA in online reviews and, at the same time, reduce the number of extracted features. Universiti Utara Malaysia Press 2022 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/29111/1/JICT%2021%2004%202022%20571-593.pdf Mat Zin, Harnani and Mustapha, Norwati and Azmi Murad, Masrah Azrifah and Mohd Sharef, Nurfadhlina (2022) A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews. Journal of Information and Communication Technology, 21 (4). pp. 571-593. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/14428 https://doi.org/10.32890/jict2022.21.4.5 https://doi.org/10.32890/jict2022.21.4.5 |
| spellingShingle | QA75 Electronic computers. Computer science Mat Zin, Harnani Mustapha, Norwati Azmi Murad, Masrah Azrifah Mohd Sharef, Nurfadhlina A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews |
| title | A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews |
| title_full | A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews |
| title_fullStr | A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews |
| title_full_unstemmed | A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews |
| title_short | A Meta-heuristic Algorithm for the Minimal High-Quality Feature Extraction of Online Reviews |
| title_sort | meta heuristic algorithm for the minimal high quality feature extraction of online reviews |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/29111/1/JICT%2021%2004%202022%20571-593.pdf https://doi.org/10.32890/jict2022.21.4.5 |
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