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

Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu


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.
Anthology ID:
S17-2110
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
664–669
Language:
URL:
https://www.aclweb.org/anthology/S17-2110
DOI:
10.18653/v1/S17-2110
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2110.pdf