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WarwickDCS : from phrase-based to target-specific sentiment recognition

Townsend, Richard; Tsakalidis, Adam; Zhou, Yiwei; Wang, Bo; Liakata, M.; Zubiaga, Arkaitz; Cristea, A.I.; Procter, Rob

WarwickDCS : from phrase-based to target-specific sentiment recognition Thumbnail


Authors

Richard Townsend

Adam Tsakalidis

Yiwei Zhou

Bo Wang

M. Liakata

Arkaitz Zubiaga

Rob Procter



Abstract

We present and evaluate several hybrid systems for sentiment identification for Twitter, both at the phrase and document (tweet) level. Our approach has been to use a novel combination of lexica, traditional NLP and deep learning features. We also analyse techniques based on syntactic parsing and tokenbased association to handle topic specific sentiment in subtask C. Our strategy has been to identify subphrases relevant to the designated topic/target and assign sentiment according to our subtask A classifier. Our submitted subtask A classifier ranked fourth in the SemEval official results while our BASELINE and µPARSE classifiers for subtask C would have ranked second.

Citation

Townsend, R., Tsakalidis, A., Zhou, Y., Wang, B., Liakata, M., Zubiaga, A., …Procter, R. (2015). WarwickDCS : from phrase-based to target-specific sentiment recognition. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) (657-663). https://doi.org/10.18653/v1/s15-2110

Presentation Conference Type Conference Paper (Published)
Conference Name 9th International Workshop on Semantic Evaluation (SemEval 2015)
Publication Date Jun 1, 2015
Deposit Date Jul 11, 2018
Publicly Available Date Jul 31, 2018
Publisher Association for Computational Linguistics
Pages 657-663
Book Title Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015).
DOI https://doi.org/10.18653/v1/s15-2110
Public URL https://durham-repository.worktribe.com/output/1145180
Related Public URLs http://wrap.warwick.ac.uk/71340/

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