Improving Arabic Sentiment Analysis Using LSTM Based on Word Embedding Models

In recent times, online users freely express their sentiments in different life aspects because of the huge increase in social networks. Sentiment Analysis (SA) is one of the main Natural Language Processing (NLP) fields thanks to its important role in identifying sentiment polarities and making decisions from the public's opinions. The Arabic language is one of the most challenging languages for SA due to its various dialects, and morphological and syntactic complexities. Deep Learning (DL) models have shown significant capabilities, especially in SA. In particular, Long Short-Term Memory (LSTM) networks have proven perfect abilities to learn sequential data. This paper proposes a comparative study result of Word2Vec and FastText word embedding models that are used to create two Arabic SA (ASA) LSTM-based approaches. The experimental results confirm that the LSTM model with FastText can significantly ameliorate the Arabic classification accuracy.