Abstract:
Sentiment analysis, a method used to classify textual content into positive, negative,
or neutral sentiments, is commonly applied to data from social media
platforms. Arabic, an official language of the United Nations, presents unique
challenges for sentiment analysis due to its complex morphology and dialectal
diversity. Compared to English, research on Arabic sentiment analysis is
relatively scarce. Transfer learning, which applies the knowledge learned from
one domain to another, can address the limitations of training time and computational
resources. However, the development of transfer learning for Arabic
sentiment analysis is still underdeveloped. In this study, we develop a
new hybrid model, RNN-BiLSTM, which merges recurrent neural networks
(RNN) and bidirectional long short-term memory (BiLSTM) networks. We used
Arabic bidirectional encoder representations from transformers (AraBERT), a
state-of-the-art Arabic language pre-trained transformer-based model, to generate
word-embedding vectors. The RNN-BiLSTM model integrates the strengths
of RNN and BiLSTM, including the ability to learn sequential dependencies
and bidirectional context. We trained the RNN-BiLSTM model on the source
domain, specifically the Arabic reviews dataset (ARD). The RNN-BiLSTMmodel
outperforms the RNN and BiLSTM models with default parameters, achieving
an accuracy of 95.75%. We further applied transfer learning to the RNN-BiLSTM
model by fine-tuning its parameters using random search. We compared the
performance of the fine-tuned RNN-BiLSTMmodel with the RNN and BiLSTM
models on two target domain datasets: ASTD and Aracust. The results showed
that the fine-tuned RNN-BiLSTM model is more effective for transfer learning,
achieving an accuracy of 95.44% and 96.19% on the ASTD and Aracust datasets,
respectively.
