@inproceedings{wang-etal-2017-tdparse,
title = "{TDP}arse: Multi-target-specific sentiment recognition on {T}witter",
author = "Wang, Bo and
Liakata, Maria and
Zubiaga, Arkaitz and
Procter, Rob",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1046",
pages = "483--493",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TDParse: Multi-target-specific sentiment recognition on Twitter
%A Wang, Bo
%A Liakata, Maria
%A Zubiaga, Arkaitz
%A Procter, Rob
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F wang-etal-2017-tdparse
%X 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.
%U https://aclanthology.org/E17-1046
%P 483-493
Markdown (Informal)
[TDParse: Multi-target-specific sentiment recognition on Twitter](https://aclanthology.org/E17-1046) (Wang et al., EACL 2017)
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.