DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis


Abstract
This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and combined supervised machine learning methods with classical linguistic resources, including bag-of-words and sentiment lexicon features.
Anthology ID:
S17-2117
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
704–708
Language:
URL:
https://www.aclweb.org/anthology/S17-2117
DOI:
10.18653/v1/S17-2117
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2117.pdf