Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis
Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people’s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content’s sentim...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3707 |
| _version_ | 1797495731762757632 |
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| author | Hager Saleh Sherif Mostafa Abdullah Alharbi Shaker El-Sappagh Tamim Alkhalifah |
| author_facet | Hager Saleh Sherif Mostafa Abdullah Alharbi Shaker El-Sappagh Tamim Alkhalifah |
| author_sort | Hager Saleh |
| collection | DOAJ |
| description | Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people’s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content’s sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model’s performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models. |
| first_indexed | 2024-03-10T01:53:49Z |
| format | Article |
| id | doaj.art-3aacc90d797c447c8ded54a849393a0f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:53:49Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3aacc90d797c447c8ded54a849393a0f2023-11-23T12:59:48ZengMDPI AGSensors1424-82202022-05-012210370710.3390/s22103707Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment AnalysisHager Saleh0Sherif Mostafa1Abdullah Alharbi2Shaker El-Sappagh3Tamim Alkhalifah4Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, EgyptFaculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, EgyptDepartment of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaFaculty of Computer Science and Engineering, Galala University, Suez 435611, EgyptDepartment of Computer, College of Science and Arts in Ar Rass, Qassim University, Buraydah 52571, Saudi ArabiaSentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people’s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content’s sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model’s performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models.https://www.mdpi.com/1424-8220/22/10/3707machine learningdeep learningensemble learningArabic sentiment analysis |
| spellingShingle | Hager Saleh Sherif Mostafa Abdullah Alharbi Shaker El-Sappagh Tamim Alkhalifah Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis Sensors machine learning deep learning ensemble learning Arabic sentiment analysis |
| title | Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis |
| title_full | Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis |
| title_fullStr | Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis |
| title_full_unstemmed | Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis |
| title_short | Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis |
| title_sort | heterogeneous ensemble deep learning model for enhanced arabic sentiment analysis |
| topic | machine learning deep learning ensemble learning Arabic sentiment analysis |
| url | https://www.mdpi.com/1424-8220/22/10/3707 |
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