Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets

2017-08-01 · SEMEVAL 2017 8 · Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu

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. However, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work.

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