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Enhancing Arabic Sentiment Analysis Using CNN Approach Based on FastText Word Embedding
Youssra Zahidi. Information System and Software Engineering Laboratory, Abdelmalek Essaadi University, Tetuan, Morocco.
Yassine Al-Amrani. Information Technology and Modeling Systems Research Team, Abdelmalek Essaadi University, Tetuan, Morocco.
Yacine El Younoussi. Information System and Software Engineering Laboratory, Abdelmalek Essaadi University, Tetuan, Morocco.
Resumen/Abstract
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Resumen / Abstract
The rapid growth of social networks allows users to express their views on various topics, significantly impacting Sentiment Analysis (SA) in Natural Language Processing (NLP). SA extracts valuable insights and aids decision-making based on public sentiment in various domains, particularly education. In other words, Arabic Sentiment Analysis (ASA) improves educational outcomes by assessing students’ learning progress and monitoring their performance. SA faces challenges with the Arabic language due to its dialects and complex morphology. Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have advanced SA by effectively capturing relevant features, outperforming traditional Machine Learning (ML) algorithms. This paper assesses FastText word embedding with a CNN-based model for Arabic Sentiment Analysis (ASA). Our results show that the CNN model with FastText achieves strong performance in both datasets, with higher classification accuracy in the first dataset compared to the second. Which confirms the effectiveness of this proposed model to evaluate website and MOOC content by measuring learner satisfaction through forum interactions and module evaluations.
Palabras Clave/Keywords
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Palabras Clave / Keywords
Arabic Sentiment Analysis, Education, Deep Learning, Convolutional Neural Networks, FastText.
Referencias/References
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Referencias / References
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Cómo citar/How to cite
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Cómo citar / How to cite
Zahidi, Y., Al-Amrani, Y., y El Younoussi, Y. (2024). Enhancing Arabic Sentiment Analysis Using CNN Approach Based on FastText Word Embedding. En C. Rusu et al., (1ª ed.), Transformación digital en la educación: innovaciones y desafíos desde los campus virtuales (pp. 109-112). Huelva (España): United Academic Journals (UA Journals). https://doi.org/10.54988/uaj.000027.019
