基于BERT模型的微博文本细粒度情感分析OA

With the rapid development of social media,Weibo,as an important platform for user information exchange and emotion expression,accumulates massive and rich text data.Based on the investigation of related technologies for Weibo sentiment analysis,this paper proposes a fine-grained sentiment analysis method based on the BERT model.Combining Weibo data crawling and preprocessing technologies,this paper constructs an efficient analysis framework.This method completes data collection through the Weibo Open API,utilizes the BERT pre-training model to realize text vectorization,and completes the precise classification of six emotions including anger,happiness,neutral,surprise,sadness,and fear based on the Transformer architecture.Meanwhile,this paper introduces sentiment dictionaries and data augmentation technologies to improve model performance,and displays analysis results by means of visualization tools.The study shows that this method achieves high classification accuracy on the SMP2020 dataset and provides a new idea for fine-grained sentiment analysis of Weibo text.