A fusion model for multi-label emotion classification based on BERT and topic clustering

Fei Ding; Xin Kang; Shun Nishide; Zhijin Guan; Fuji Ren

doi:10.1117/12.2579255

12 October 2020 A fusion model for multi-label emotion classification based on BERT and topic clustering

Fei Ding, Xin Kang, Shun Nishide, Zhijin Guan, Fuji Ren

Proceedings Volume 11574, International Symposium on Artificial Intelligence and Robotics 2020; 115740D (2020) https://doi.org/10.1117/12.2579255
Event: International Symposium on Artificial Intelligence and Robotics (ISAIR), 2020, Kitakyushu, Japan

Abstract

As one of the most critical tasks of natural language processing (NLP), emotion classification has a wide range of applications in many fields. However, restricted by corpus, semantic ambiguity, and other constraints, researchers in emotion classification face many difficulties, and the accuracy of multi-label emotion classification is not ideal. In this paper, to improve the accuracy of multi-label emotion classification, especially when semantic ambiguity occurs, we proposed a fusion model for text based on self-attention and topic clustering. We use the Pre-trained BERT to extract the hidden emotional representations of the sentence, and use the improved LDA topic model to cluster the topics of different levels of text. Then we fuse the hidden representations of the sentence and use a classification neural network to calculate the multi-label emotional intensity of the sentence. After testing on the Chinese emotion corpus Ren_CECPs corpus, extensive experimental results demonstrate that our model outperforms several strong baselines and related works. The F1-score of our model reaches 0.484, which is 0.064 higher than the best results in similar studies.

Citation Download Citation

Fei Ding, Xin Kang, Shun Nishide, Zhijin Guan, and Fuji Ren "A fusion model for multi-label emotion classification based on BERT and topic clustering", Proc. SPIE 11574, International Symposium on Artificial Intelligence and Robotics 2020, 115740D (12 October 2020); https://doi.org/10.1117/12.2579255

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