Word Embeddings and Neural Network Architectures for Arabic Sentiment Analysis
Mohamed Fawzy, Mohamed Waleed Fakhr, Mohamed A. Aborizka · 2020
With the development of the new era of technology and social networks, forums, blogs, and online sales, combined with the increase of Arabs expressing their opinions, it became an urgent matter to do this research. This paper investigates different deep neural network architectures to perform Arabic sentiment classification combined with word embedding approaches. The models used are Recurrent Neural network (RNN), Bidirectional multi-layer long short-term memory (LSTM), and FastText. Different hyperparameters are used to train each model. In addition, a neural network of Multi-Layer Bidirectional Long Short-Term Memory trained on top of Glove Arabic word embedding with 1.75 billion tokens and 1.5 million words beat the existing methods.