Extending the Knowledge of the Arabic Sentiment Classification Using a Foreign External Lexical Source

Abstract

This article introduces a methodology for analyzing sentiment in Arabic text using a global foreign lexical source. Our method leverages the available resource in another language such as the SentiWordNet in English to the limited language resource that is Arabic. The knowledge that is taken from the external resource will be injected into the feature model whilethe machine-learning-based classifier is trained. The first step of our method is to build the bag-of-words (BOW) model of the Arabic text. The second step calculates the score of polarity using translation machine technique and English SentiWordNet. The scores for each text will be added to the model in three pairs for objective, positive, and negative. The last step of our method involves training the ML classifier on that model to predict the sentiment of the Arabic text. Our method increases the performance compared with the baseline model that is BOW in most cases. In addition, it seems a viable approach to sentiment analysis in Arabic text where there is limitation of the available resource.