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
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 664–669
- Language:
- URL:
- https://aclanthology.org/S17-2110
- DOI:
- 10.18653/v1/S17-2110
- Cite (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.
- Cite (Informal):
- Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets (Mulki et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/S17-2110.pdf