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

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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.

Item Type: Conference Item (Paper)
Alternative Title:
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Online social networks -- Data processing, Natural language processing (Computer science) , Grammar, Comparative and general -- Parsing
Journal or Publication Title: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
Publisher: Association for Computational Linguistics
Official Date: June 2015
Dates:
Date
Event
June 2015
Available
Page Range: pp. 657-663
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons open licence)
Date of first compliant deposit: 5 August 2016
Date of first compliant Open Access: 5 August 2016
RIOXX Funder/Project Grant:
Project/Grant ID
RIOXX Funder Name
Funder ID
EP/ L016400/1
[EPSRC] Engineering and Physical Sciences Research Council
Conference Paper Type: Paper
Title of Event: 9th International Workshop on Semantic Evaluation (SemEval 2015)
Type of Event: Workshop
Location of Event: Denver, Colorado
Date(s) of Event: 4-5 Jun 2015
Related URLs:
URI: https://wrap.warwick.ac.uk/71340/

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