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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/S17-2117.pdf