Abstract
We describe our approach for the SemEval-2014 task 9: Sentiment Analysis in Twitter. We make use of an ensemble learning method for sentiment classification of tweets that relies on varied features such as feature hashing, part-of-speech, and lexical features. Our system was evaluated in the Twitter message-level task.
Cite
CITATION STYLE
Silva, N. F. F., Hruschka, E. R., & Rafael Hruschka, E. (2014). Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble. In 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 123–128). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/s14-2017
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