Arabic Sentiment Analysis Using Supervised Classification

Abstract

Sentiment analysis is a process during which the polarity (i.e. positive, negative or neutral) of a given text is determined. In general there are two approaches to address this problem, namely, machine learning approach or lexicon based approach. The current paper deals with sentiment analysis in Arabic reviews from a machine learning perspective. Three classifiers were applied on an in-house developed dataset of tweets/comments. In particular, the Naïve Bayes, SVM and K-Nearest Neighbor classifiers were run on this dataset. The results show that SVM gives the highest precision while KNN (K=10) gives the highest Recall.