TDParse: Multi-target-specific sentiment recognition on Twitter

Bo Wang, Maria Liakata, Arkaitz Zubiaga, Rob Procter


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/E17-1046.pdf