Abstract
This study focuses on sentiment analysis in Arabic texts using the embedding approach of AraBERT and various neural network architectures, including CNN, LSTM, GRU, BI-LSTM, and BI-GRU. The objective was to predict the sentiments associated with each comment. According to the findings, combining the embedding of AraBERT with the GRU and BI-GRU models demonstrated superior performance in precision, recall, F1 score, and accuracy measures. Nonetheless, it's worth noting that the AraBERT + LSTM model distinguishes itself due to its notably shorter learning time. This research opens new perspectives for sentiment analysis in Arabic using deep learning.
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Rachidi, A., Ouacha, A., El Ghmary, M. (2024). Sentiment Analysis by Deep Learning Techniques. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-031-48465-0_51
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