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
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
704–708
Language:
URL:
https://aclanthology.org/S17-2117
DOI:
10.18653/v1/S17-2117
Bibkey:
Cite (ACL):
Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, and Avi Arampatzis. 2017. DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 704–708, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis (Symeonidis et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2117.pdf