Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets
Hala Mulki, Hatem Haddad, Mourad Gridach, İsmail Babaoğlu · 2017
In this paper, we present our contribution in SemEval 2017 international workshop.We have tackled task 4 entitled "Sentiment analysis in Twitter", specifically subtask 4A-Arabic.We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies.In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features.The final submission was selected upon the best performance achieved by the supervised learning-based model.Nevertheless, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work.