Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis
Sentiment Analysis (SA) is a key area of data mining that focuses on analyzing emotions in social media documents. However, these documents often contain redundant and irrelevant features, leading to high-dimensional datasets that reduce SA performance. Efficient sentiment feature selection (FS) is...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000906 |
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| author | Tamara Amjad Al-Qablan Mohd Halim Mohd Noor Mohammed Azmi Al-Betar Ahamad Tajudin Khader |
| author_facet | Tamara Amjad Al-Qablan Mohd Halim Mohd Noor Mohammed Azmi Al-Betar Ahamad Tajudin Khader |
| author_sort | Tamara Amjad Al-Qablan |
| collection | DOAJ |
| description | Sentiment Analysis (SA) is a key area of data mining that focuses on analyzing emotions in social media documents. However, these documents often contain redundant and irrelevant features, leading to high-dimensional datasets that reduce SA performance. Efficient sentiment feature selection (FS) is crucial for reducing data dimensionality and isolating relevant features to improve results. This study aims to enhance FS performance by addressing the population diversity issues in the Adaptive β Binary Gray Wolf Optimization (Aβ-BGWO) algorithm, which struggles to escape local optima. To address this, a hybrid algorithm combining Aβ-BGWO with Harris Hawks Optimization (HHO) is proposed, resulting in the Aβ-BGWHHO approach for optimized FS in SA. The effectiveness of selected features is evaluated using the KNN classifier, and performance is assessed across 18 UCI datasets, comparing it with recent metaheuristic FS algorithms. Population convergence and diversity are measured using dimension-wise diversity to evaluate exploration and exploitation behavior. Furthermore, four Arabic benchmark datasets and six state-of-the-art optimization techniques are used for SA assessment. Experimental results show that the Aβ-BGWHHO algorithm outperforms other algorithms regarding accuracy, feature reduction, and fitness value. The hybrid approach enhances population diversity, allowing the algorithm to effectively balance exploration and exploitation, resulting in superior performance. The integration of HHO significantly improves the algorithm’s ability to escape local optima, making the binary hybrid Aβ-BGWHHO algorithm a more effective tool for SA, addressing Aβ-BGWO’s limitations and enhancing FS outcomes. |
| format | Article |
| id | doaj-art-bc994868ce114ef9b9ea7301e0a4dde4 |
| institution | DOAJ |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-bc994868ce114ef9b9ea7301e0a4dde42025-08-20T02:58:03ZengElsevierResults in Control and Optimization2666-72072025-09-012010060410.1016/j.rico.2025.100604Improved gray wolf harris hawk algorithm based feature selection for sentiment analysisTamara Amjad Al-Qablan0Mohd Halim Mohd Noor1Mohammed Azmi Al-Betar2Ahamad Tajudin Khader3Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, 50, Jordan; Corresponding author.School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, MalaysiaArtificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, 346, United Arab Emirates; Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, ThailandSchool of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, MalaysiaSentiment Analysis (SA) is a key area of data mining that focuses on analyzing emotions in social media documents. However, these documents often contain redundant and irrelevant features, leading to high-dimensional datasets that reduce SA performance. Efficient sentiment feature selection (FS) is crucial for reducing data dimensionality and isolating relevant features to improve results. This study aims to enhance FS performance by addressing the population diversity issues in the Adaptive β Binary Gray Wolf Optimization (Aβ-BGWO) algorithm, which struggles to escape local optima. To address this, a hybrid algorithm combining Aβ-BGWO with Harris Hawks Optimization (HHO) is proposed, resulting in the Aβ-BGWHHO approach for optimized FS in SA. The effectiveness of selected features is evaluated using the KNN classifier, and performance is assessed across 18 UCI datasets, comparing it with recent metaheuristic FS algorithms. Population convergence and diversity are measured using dimension-wise diversity to evaluate exploration and exploitation behavior. Furthermore, four Arabic benchmark datasets and six state-of-the-art optimization techniques are used for SA assessment. Experimental results show that the Aβ-BGWHHO algorithm outperforms other algorithms regarding accuracy, feature reduction, and fitness value. The hybrid approach enhances population diversity, allowing the algorithm to effectively balance exploration and exploitation, resulting in superior performance. The integration of HHO significantly improves the algorithm’s ability to escape local optima, making the binary hybrid Aβ-BGWHHO algorithm a more effective tool for SA, addressing Aβ-BGWO’s limitations and enhancing FS outcomes.http://www.sciencedirect.com/science/article/pii/S2666720725000906Sentiment analysisFeature selectionGray wolf optimizerHarris Hawk optimizerExplorationExploitation |
| spellingShingle | Tamara Amjad Al-Qablan Mohd Halim Mohd Noor Mohammed Azmi Al-Betar Ahamad Tajudin Khader Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis Results in Control and Optimization Sentiment analysis Feature selection Gray wolf optimizer Harris Hawk optimizer Exploration Exploitation |
| title | Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis |
| title_full | Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis |
| title_fullStr | Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis |
| title_full_unstemmed | Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis |
| title_short | Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis |
| title_sort | improved gray wolf harris hawk algorithm based feature selection for sentiment analysis |
| topic | Sentiment analysis Feature selection Gray wolf optimizer Harris Hawk optimizer Exploration Exploitation |
| url | http://www.sciencedirect.com/science/article/pii/S2666720725000906 |
| work_keys_str_mv | AT tamaraamjadalqablan improvedgraywolfharrishawkalgorithmbasedfeatureselectionforsentimentanalysis AT mohdhalimmohdnoor improvedgraywolfharrishawkalgorithmbasedfeatureselectionforsentimentanalysis AT mohammedazmialbetar improvedgraywolfharrishawkalgorithmbasedfeatureselectionforsentimentanalysis AT ahamadtajudinkhader improvedgraywolfharrishawkalgorithmbasedfeatureselectionforsentimentanalysis |