Study on text classification model combining BERT and convolutional neural network

Text classification has been widely used in many practical scenarios such as sentiment analysis and topic classification, and is an important task in the field of natural language processing. In recent years, research on pre-training model based on Bidirectional Encoder Representations from Transformers (BERT) has made remarkable progress. However, at present, most studies are based on open source databases. In this study, a text classification model combining BERT model and convolutional neural network (CNN) is proposed to complete the text emotion classification task through the self-built dialect text database. This paper demonstrates the effectiveness and superiority of this model in Chinese text classification tasks by comparing experiments and performance evaluation.