Abstract
In this paper, we describe how we created a meta-classifier to detect the message-level sentiment of tweets. We participated in SemEval-2014 Task 9B by combining the results of several existing classifiers using a random forest. The results of 5 other teams from the competition as well as from 7 general-purpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F1 score points.
Cite
CITATION STYLE
Dürr, O., Uzdilli, F., & Cieliebak, M. (2014). JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest. In 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings (pp. 366–369). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/s14-2062
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