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

Townsend, Richard and Tsakalidis, Adam and Zhou, Yiwei and Wang, Bo and Liakata, M. and Zubiaga, Arkaitz and Cristea, A. I. and Procter, Rob (2015) 'WarwickDCS : from phrase-based to target-specific sentiment recognition.', in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). , pp. 657-663.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial Share Alike.
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Publisher statement:Available under a Creative Commons Attribution Non-commercial Share Alike License.
Date accepted:No date available
Date deposited:31 July 2018
Date of first online publication:June 2015
Date first made open access:No date available

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