关键词: 深度学习, 文本情感分析, 基于变换器的双向编码器表征技术（BERT）, 卷积神经网络（CNN）, 协同结构

Abstract:

Due to the introduction of the Bidirectional Encoder Representations from Transformers（BERT）, the traditional neural network method for solving sentence-level text sentiment analysis problems has been changed. The current deep learning model BERT’s own learning mode is unsupervised learning, which needs to rely on subsequent tasks to complete the reasoning and decision-making links, so there is a problem of lack of target domain knowledge. This paper proposes a Multi-level collaborative Convolutional Neural Network（MCNN） model, which can learn different levels of emotional features to supplement domain knowledge, and uses the BERT pre-training model to provide word vectors, and the sentence is dynamically adjusted through the dynamic adjustment of the BERT learning ability. The real sentiment orientation is embedded in the model, and finally the feature information output by the different levels of the model is combined with the output information of the bidirectional long-term short-term memory network to calculate the final text sentiment orientation. Experimental results show that even in corpora of different languages, this model has significantly improved the ability of emotional polarity classification compared with traditional neural networks and the recently proposed BERT-based deep learning model.

Key words: deep learning, text sentiment analysis, Bidirectional Encoder Representations from Transformers（BERT）, Convolutional Neural Network（CNN）, collaborative structure