Abstract
Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for single-target sentiment.- Anthology ID:
- E17-1046
- 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:
- 483–493
- Language:
- URL:
- https://aclanthology.org/E17-1046
- DOI:
- Cite (ACL):
- Bo Wang, Maria Liakata, Arkaitz Zubiaga, and Rob Procter. 2017. TDParse: Multi-target-specific sentiment recognition on Twitter. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 483–493, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- TDParse: Multi-target-specific sentiment recognition on Twitter (Wang et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/E17-1046.pdf