Abstract
We consider entity-level sentiment analysis in Arabic, a morphologically rich language with increasing resources. We present a system that is applied to complex posts written in response to Arabic newspaper articles. Our goal is to identify important entity “targets” within the post along with the polarity expressed about each target. We achieve significant improvements over multiple baselines, demonstrating that the use of specific morphological representations improves the performance of identifying both important targets and their sentiment, and that the use of distributional semantic clusters further boosts performances for these representations, especially when richer linguistic resources are not available.- Anthology ID:
- E17-1094
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1002–1013
- Language:
- URL:
- https://aclanthology.org/E17-1094
- DOI:
- Cite (ACL):
- Noura Farra and Kathy McKeown. 2017. SMARTies: Sentiment Models for Arabic Target entities. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1002–1013, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- SMARTies: Sentiment Models for Arabic Target entities (Farra & McKeown, EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-1094.pdf
- Data
- SemEval-2014 Task-4