Abstract
In Supervised-learning of emotions from human language, to keep the emotion labels balanced in the training set is a challenging task since emotion labels are highly biased in raw data of human language. In this paper, we propose a novel method based on active learning to partially inhibit the polarization of text samples with more frequently observed emotion labels for constructing the training set, and to encourage the selection of samples with less frequently observed emotion labels. For each batch of unlabeled samples, the selected samples by our approach are given the ground truth emotion labels from human experts before they are merged to the training data. Our experiment of multi-label emotion classification on Chinese Weibo messages suggests that the proposed method is effective in constructing the label-balanced training set for text emotion classification, and the supervised text emotion classification results have been steadily improved with such training set.
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Notes
- 1.
The argpartition(F(X), n) function selects n elements x from X which have larger scores F(·) than the other elements in X.
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Acknowledgements
This research has been partially supported by the Ministry of Education, Science, Sports and Culture of Japan, Grant-in-Aid for Scientific Research(A), 15H01712.
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Shi, X., Kang, X., Liao, P., Ren, F. (2021). Building Label-Balanced Emotion Corpus Based on Active Learning for Text Emotion Classification. In: Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2019. Studies in Computational Intelligence, vol 917. Springer, Cham. https://doi.org/10.1007/978-3-030-56178-9_2
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