NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification
Background and Objectives: With the extensive web applications, review sentiment classification has attracted increasing interest among text mining works. Traditional approaches did not indicate multiple relationships connecting words while emphasizing the preprocessing phase and data reduction tech...
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| Format: | Article |
| Language: | English |
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Shahid Rajaee Teacher Training University
2020-07-01
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| Series: | Journal of Electrical and Computer Engineering Innovations |
| Subjects: | |
| Online Access: | https://jecei.sru.ac.ir/article_1460_3019af0b00a65c1e5518a2778948a48e.pdf |
| _version_ | 1818200933900746752 |
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| author | R. Asgarnezhad A. Monadjemi M. SoltanAghaei |
| author_facet | R. Asgarnezhad A. Monadjemi M. SoltanAghaei |
| author_sort | R. Asgarnezhad |
| collection | DOAJ |
| description | Background and Objectives: With the extensive web applications, review sentiment classification has attracted increasing interest among text mining works. Traditional approaches did not indicate multiple relationships connecting words while emphasizing the preprocessing phase and data reduction techniques, making a huge performance difference in classification. Methods: This study suggests a model as an efficient model for sentiment classification combining preprocessing techniques, sampling methods, feature selection methods, and ensemble supervised classification to increase the classification performance. In the feature selection phase of the proposed model, we applied n-grams, which is a computational method, to optimize the feature selection procedure by extracting features based on the relationships of the words. Then, the best-selected feature through the particle swarm optimization algorithm to optimize the feature selection procedure by iteratively trying to improve feature selection. Results: In the experimental study, a comprehensive range of comparative experiments conducted to assess the effectiveness of the proposed model using the best in the literature on Twitter datasets. The highest performance of the proposed model obtains 97.33, 92.61, 97.16, and 96.23% in terms of precision, accuracy, recall, and f-measure, respectively.Conclusion: The proposed model classifies the sentiment of tweets and online reviews through ensemble methods. Besides, two sampling techniques had applied in the preprocessing phase. The results confirmed the superiority of the proposed model over state-of-the-art systems. |
| first_indexed | 2024-12-12T02:45:32Z |
| format | Article |
| id | doaj.art-905c9025e0c245c6a4752a17f97b87d3 |
| institution | Directory Open Access Journal |
| issn | 2322-3952 2345-3044 |
| language | English |
| last_indexed | 2024-12-12T02:45:32Z |
| publishDate | 2020-07-01 |
| publisher | Shahid Rajaee Teacher Training University |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering Innovations |
| spelling | doaj.art-905c9025e0c245c6a4752a17f97b87d32022-12-22T00:41:02ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442020-07-018218319210.22061/jecei.2020.7295.3791460NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text ClassificationR. Asgarnezhad0A. Monadjemi1M. SoltanAghaei2Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, IranFaculty of Computer Engineering, University of Isfahan, Isfahan, Iran and Senior Lecturer, School of continuing and lifelong education, National University of Singapore, Singapore, 119077Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, IranBackground and Objectives: With the extensive web applications, review sentiment classification has attracted increasing interest among text mining works. Traditional approaches did not indicate multiple relationships connecting words while emphasizing the preprocessing phase and data reduction techniques, making a huge performance difference in classification. Methods: This study suggests a model as an efficient model for sentiment classification combining preprocessing techniques, sampling methods, feature selection methods, and ensemble supervised classification to increase the classification performance. In the feature selection phase of the proposed model, we applied n-grams, which is a computational method, to optimize the feature selection procedure by extracting features based on the relationships of the words. Then, the best-selected feature through the particle swarm optimization algorithm to optimize the feature selection procedure by iteratively trying to improve feature selection. Results: In the experimental study, a comprehensive range of comparative experiments conducted to assess the effectiveness of the proposed model using the best in the literature on Twitter datasets. The highest performance of the proposed model obtains 97.33, 92.61, 97.16, and 96.23% in terms of precision, accuracy, recall, and f-measure, respectively.Conclusion: The proposed model classifies the sentiment of tweets and online reviews through ensemble methods. Besides, two sampling techniques had applied in the preprocessing phase. The results confirmed the superiority of the proposed model over state-of-the-art systems.https://jecei.sru.ac.ir/article_1460_3019af0b00a65c1e5518a2778948a48e.pdftext classificationsampling techniquefeature selectionoptimization algorithmtwitter |
| spellingShingle | R. Asgarnezhad A. Monadjemi M. SoltanAghaei NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification Journal of Electrical and Computer Engineering Innovations text classification sampling technique feature selection optimization algorithm |
| title | NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification |
| title_full | NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification |
| title_fullStr | NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification |
| title_full_unstemmed | NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification |
| title_short | NSE-PSO: Toward an Effective Model Using Optimization Algorithm and Sampling Methods for Text Classification |
| title_sort | nse pso toward an effective model using optimization algorithm and sampling methods for text classification |
| topic | text classification sampling technique feature selection optimization algorithm |
| url | https://jecei.sru.ac.ir/article_1460_3019af0b00a65c1e5518a2778948a48e.pdf |
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