applied sciences  
Article
Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models  
[PERSON],*, [PERSON], [PERSON], [PERSON],* and [PERSON]  
1 Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 1974531, Egypt 2 Department of Computer Science and Information Technology, College of Applied, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 3 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia 4 Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt 5 Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt *Correspondence: [EMAIL_ADDRESS] (H.S. ); [EMAIL_ADDRESS] (A.O.A.) Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdfpdf/pdf/pdfpdf/pdf/pdfpdf/pdfpdf/pdfpdf/pdfpdf/](https://www.mdpi.com/journals/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdfpdf/pdf/pdfpdf/pdf/pdfpdf/pdfpdf/pdfpdf/pdfpdf/)]([https://www.mdpi.com/journals/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdfpdf/pdf/pdfpdf/pdf/pdfpdf/pdfpdf/pdfpdf/pdfpdf/](https://www.mdpi.com/journals/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdf/pdfpdf/pdf/pdfpdf/pdf/pdfpdf/pdfpdf/pdfpdf/pdfpdf/))  
(NLP) in which advanced data mining and machine learning models are used to measure sentiments, emotional responses, and attitudes in a variety of domains, such as service quality, product acceptance, price trends, and popular support of government actions and events [2]. Arabic has several irregular forms, intricate morpho-syntactic agreement rules, and a variety of linguistic varieties with no established writing standards. Learning stable general models over Arabic text may be difficult without suitable processing and handling. In terms of Sentiment Lexicons and Annotated Sentiment Corpora, Arabic Sentiment Analysis has fewer resources than English Sentiment Analysis does. Arabic Sentiment Analysis has garnered a lot of interest due to these challenges. (ASA) [3]. Deep learning (DL) and machine learning (ML) techniques can provide an automated mechanism for processing and extracting valuable data and sentiments from enormous amounts of text, and [1]. Recently, convolutional neural network (CNN) models and hybrid models of CNN and other DL models, such as long short-term memory (LSTM), provided significant improvements in performance in sentiment analysis [4; 5; 6]. CNN uses deep layers such as convolutional, pooling, and fully connected layers to extract more profound and essential features from text data. LSTM has a memory state that effectively memorizes necessary information in the text and understands the meaning of the whole sentence. For instance, [PERSON] et al. [7] proposed a hybrid CNN-LSTM model employing word2 vec word embeddings for the binary classification of Arabic attitudes, combining the strengths of both CNN and LSTM. [PERSON] et al. [8] proposed a hybrid LSTM-RNN model based on LSTM and recurrent neural network (RNN) to analyze Arabic sentiment. The performance of the hybrid DL models can be improved by integrating more than one model [4], constituting an ensemble. An ensemble classifier is formed by combining the results of several classifiers to allow component models to balance out each other's shortcomings. In the literature on machine learning, ensemble learning techniques are receiving more attention.