@inproceedings{symeonidis-etal-2017-duth,
title = "{DUTH} at {S}em{E}val-2017 Task 4: A Voting Classification Approach for {T}witter Sentiment Analysis",
author = "Symeonidis, Symeon and
Effrosynidis, Dimitrios and
Kordonis, John and
Arampatzis, Avi",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2117",
doi = "10.18653/v1/S17-2117",
pages = "704--708",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis
%A Symeonidis, Symeon
%A Effrosynidis, Dimitrios
%A Kordonis, John
%A Arampatzis, Avi
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F symeonidis-etal-2017-duth
%X 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.
%R 10.18653/v1/S17-2117
%U https://aclanthology.org/S17-2117
%U https://doi.org/10.18653/v1/S17-2117
%P 704-708
Markdown (Informal)
[DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis](https://aclanthology.org/S17-2117) (Symeonidis et al., SemEval 2017)
ACL