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In order to analyze the emotional tendency of the network public opinion during the emergency, so as to adjust people􀆳s emotions more effectively, and maintain social stability, a deep learning method integrating BERT models and multi-channel convolutional neural networks for fine-grained emotion classification were proposed to obtain more abundant information on text semantic features. The input text was encoded by BERT to enhance the semantic feature representation of the text, and the text features were learned by parallel convolution layers with multiple convolution cores of different sizes to capture the deep features of the text and improve the performance of the model in text classification. Comparative experiments show that the proposed model outperforms the traditional emotion classification model in terms of accuracy, recall rate and F1 value, and can significantly improve the performance of finegrained emotion classification. In addition, the influence of emoticons on the fine-grained emotion classification model was also explored. The experimental results show that the conversion of emoticons into text can enhance the emotion feature extraction ability of the text and improve the classification performance of the model. [ABSTRACT FROM AUTHOR]

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