{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:25:40Z","timestamp":1776680740944,"version":"3.51.2"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people\u2019s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22103707","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"3707","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7758-0811","authenticated-orcid":false,"given":"Hager","family":"Saleh","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2520-4005","authenticated-orcid":false,"given":"Sherif","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alharbi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9705-1477","authenticated-orcid":false,"given":"Shaker","family":"El-Sappagh","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt"},{"name":"Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8407-2068","authenticated-orcid":false,"given":"Tamim","family":"Alkhalifah","sequence":"additional","affiliation":[{"name":"Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Buraydah 52571, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shah, D., Isah, H., and Zulkernine, F. 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