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JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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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 generalpurpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F 1 score points.
doi:10.3115/v1/s14-2062
dblp:conf/semeval/DurrUC14
fatcat:4vpezvui5jaivkfkq42hrufpra