Deep learning approaches for Arabic sentiment analysis

Social media are considered an excellent source of information and can provide opinions, thoughts and insights toward various important topics. Sentiment analysis becomes a hot topic in research due to its importance in making decisions based on opinions derived from analyzing the user's contents on social media. Although the Arabic language is one of the widely spoken languages used for content sharing across the social media, the sentiment analysis on Arabic contents is limited due to several challenges including the morphological structures of the language, the varieties of dialects and the lack of the appropriate corpora. Hence, the rapid increase in research in Arabic sentiment analysis is grown slowly in contrast to other languages such as English. The contribution of this paper is twofold: First, we introduce a corpus of forty thousand labeled Arabic tweets spanning several topics. Second, we present three deep learning models, namely CNN, LSTM and RCNN, for Arabic sentiment analysis. With the help of word embedding, we validate the performance of the three models on the proposed corpus. The experimental results indicate that LSTM with an average accuracy of 81.31% outperforms CNN and RCNN. Also, applying data augmentation on the corpus increases LSTM accuracy by 8.3%.