Chinese Fine-Grained Sentiment Classification Based on Pre-trained Language Model and Attention Mechanism

With the development of technology and the popularization of the Internet, the use of online platforms is gradually rising in all walks of life. People participate in the use of the platform and post comments, and the information interaction generated by this will affect other people's views on the matter in the future. It can be seen that the analysis of these subjective evaluation information is particularly important. Sentiment analysis research has gradually developed into specific aspects of sentiment judgment, which is called fine-grained sentiment classification. Nowadays, China has a large population of potential customers and Chinese fine-grained sentiment classification has become a current research hotspot. Aiming at the problem of low accuracy and poor classification effect of existing models in deep learning, this paper conducts experimental research based on the merchant review information data set of Dianping. The BERT-ftfl-SA model is proposed and integrate the attention mechanism to further strengthen the data characteristics. Compared with traditional models such as SVM and FastText, its classification effect is significantly improved. It is concluded that the improved BERT-ftfl-SA fine-grained sentiment classification model can achieve efficient sentiment classification of Chinese text.