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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CITATION STYLE
Symeonidis, S., Effrosynidis, D., Kordonis, J., & Arampatzis, A. (2017). DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 704–708). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S17-2117
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