« Previous Article
Next Article »

Review Article 


A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques

Sara Manour Almutairi, Fahad Mazead Alotaibi.


Abstract
The Internet has a huge amount of information when it comes to analysis, much of which is valuable and significant. Arabic Sentiment Analysis (SA) is a method responsible for analyzing people’s thoughts, feelings, and responses to a variety of products and services on social networking and commercial sites. Several researchers utilize sentiment analysis to determine the opinions of customers in various areas, including e-marketing, business, and other fields. Deep learning (DL) is a useful technology for developing sentiment analysis models to improve e-marketing operations. There are a few studies targeting Arabic sentiment analysis (ASA) in e-marketing using deep learning algorithms. Due to a number of difficulties in the Arabic language, such as the language’s morphological features, the diversity of dialects, and the absence of suitable corpora, sentiment analysis on Arabic material is restricted. In this paper, we will compare several Arabic sentiment analysis models. Also, we discuss the deep learning algorithms that are employed in Arabic sentiment analysis. The domain of the collected papers is Arabic sentiment analysis in e-marketing using deep learning. Our first contribution is to introduce and present deep learning models that are used in ASA. Secondly, investigate and study Arabic datasets utilized for Arabic sentence analysis. We create and develop a new Arabic dataset for Saudi Arabian communication companies, namely Sara-Dataset, to increase the quality and quantity of their services. Third, each collected study is assessed in terms of its methodology, contributions, deep learning techniques, performance, Arabic datasets in emarketing, and potential improvements in developing Arabic sentiment analysis models. Fourth, we analyzed several papers’ performance in terms of accuracy, F-measure, recall, pre-procession, and area under the curve (AUC). Also, our comparative analysis includes feature selection (e.g., domain-specific selection) methods that are used in Arabic sentiment analysis. Fifth, we also discuss how to improve Arabic sentiment analysis using preprocessing techniques (e.g., word embedding). Finally, we provide a design model for analyzing Arabic sentiment about communications services provided by Saudi Arabian enterprises.

Key words: Deep Learning; Comparative; Arabic Sentiment Analysis; E−marketing; Accuracy; Dataset; Feature Selection; Pre−processing CNN; LSTM


 
ARTICLE TOOLS
Abstract
PDF Fulltext
How to cite this articleHow to cite this article
Citation Tools
Related Records
 Articles by Sara Manour Almutairi
Articles by Fahad Mazead Alotaibi
on Google
on Google Scholar


How to Cite this Article
Pubmed Style

Almutairi SM, Alotaibi FM. A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. Journal of Engineering and Applied Sciences. 2023; 10(1): 19-34. doi:10.5455/jeas.2023050102


Web Style

Almutairi SM, Alotaibi FM. A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. https://jecasmu.org/?mno=124815 [Access: September 13, 2024]. doi:10.5455/jeas.2023050102


AMA (American Medical Association) Style

Almutairi SM, Alotaibi FM. A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. Journal of Engineering and Applied Sciences. 2023; 10(1): 19-34. doi:10.5455/jeas.2023050102



Vancouver/ICMJE Style

Almutairi SM, Alotaibi FM. A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. Journal of Engineering and Applied Sciences. (2023), [cited September 13, 2024]; 10(1): 19-34. doi:10.5455/jeas.2023050102



Harvard Style

Almutairi, S. M. & Alotaibi, . F. M. (2023) A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. Journal of Engineering and Applied Sciences, 10 (1), 19-34. doi:10.5455/jeas.2023050102



Turabian Style

Almutairi, Sara Manour, and Fahad Mazead Alotaibi. 2023. A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. Journal of Engineering and Applied Sciences, 10 (1), 19-34. doi:10.5455/jeas.2023050102



Chicago Style

Almutairi, Sara Manour, and Fahad Mazead Alotaibi. "A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques." Journal of Engineering and Applied Sciences 10 (2023), 19-34. doi:10.5455/jeas.2023050102



MLA (The Modern Language Association) Style

Almutairi, Sara Manour, and Fahad Mazead Alotaibi. "A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques." Journal of Engineering and Applied Sciences 10.1 (2023), 19-34. Print. doi:10.5455/jeas.2023050102



APA (American Psychological Association) Style

Almutairi, S. M. & Alotaibi, . F. M. (2023) A Comparative Analysis for Arabic Sentiment Analysis Models In E-Marketing Using Deep Learning Techniques. Journal of Engineering and Applied Sciences, 10 (1), 19-34. doi:10.5455/jeas.2023050102