Improving Arabic sentiment analysis with sentiment-specific embeddings
A. Aziz Altowayan, Ashraf Elnagar · 2017
Many natural language processing tasks depend on the quality of word representations in order to achieve good results. To build good word vectors for Arabic sentiment analysis there are various factors to consider, such as the corpus choice, text preprocessing, and the choice of the training model. In this paper, we demonstrate how to achieve better sentiment analysis results by building sentiment-specific embeddings trained using the unsupervised fastText tool for both CBOW and skip-gram models. The results show that fastText embedding models are a valuable alternative to recover semantic and syntactic information of standard Arabic as well as a dialectal language. Indeed, fastText is more relevant to morphologically rich languages such as Arabic language. In order to further validate the effectiveness of the resulting word embeddings, we implemented polarity sentiment classifiers and compared their performance to 2 similar models reported in the literature on three different datasets. The performance evaluation results confirm that our approach outperforms counterpart ones on relevant datasets with an improvement margin of up to 5% using F1-score.