Abstract
This paper describes the enhancements made to our GU-MLT-LT system (Günther and Furrer, 2013) for the SemEval-2014 re-run of the SemEval-2013 shared task on sentiment analysis in Twitter. The changes include the usage of a Twitter-specific tokenizer, additional features and sentiment lexica, feature weighting and random subspace learning. The improvements result in an increase of 4.18 F-measure points on this year’s Twitter test set, ranking 3rd.
Cite
CITATION STYLE
Günther, T., Vancoppenolle, J., & Johansson, R. (2014). RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages. In 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 497–502). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/s14-2086
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