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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets
%A Mulki, Hala
%A Haddad, Hatem
%A Gridach, Mourad
%A Babaoglu, Ismail
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F mulki-etal-2017-tw
%X 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.
%R 10.18653/v1/S17-2110
%U https://aclanthology.org/S17-2110
%U https://doi.org/10.18653/v1/S17-2110
%P 664-669

Markdown (Informal)

[Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets](https://aclanthology.org/S17-2110) (Mulki et al., SemEval 2017)

• Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets (Mulki et al., SemEval 2017)

ACL

• Hala Mulki, Hatem Haddad, Mourad Gridach, and Ismail Babaoglu. 2017. Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 664–669, Vancouver, Canada. Association for Computational Linguistics.