现代信息科技2026，Vol.10Issue(4)：112-115,121,5.DOI:10.19850/j.cnki.2096-4706.2026.04.019
基于BERT模型的微博文本细粒度情感分析
Fine-grained Sentiment Analysis of Weibo Text Based on BERT Model
摘要
Abstract
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.关键词
微博情感分析/细粒度情感/BERT模型/社交媒体Key words
Weibo sentiment analysis/fine-grained sentiment/BERT model/social media分类
信息技术与安全科学引用本文复制引用
张逸民,李野..基于BERT模型的微博文本细粒度情感分析[J].现代信息科技,2026,10(4):112-115,121,5.基金项目
上海杉达学院校级重点课程项目(A020201.24.049) （A020201.24.049）
上海杉达学院科研基金项目-基于强化学习协同进化算法求解时间表调度问题研究 （）
