Network public opinion analysis is obtained by a combination of natural language processing (NLP) and public opinion supervision, and is crucial for monitoring public mood and trends. Therefore, network public opinion analysis can identify and solve potential and budding social problems. This study aims to realize an analysis of Chinese sentiment in social media reviews using a long short-term memory network (LSTM) model. The dataset was obtained from Sina Weibo using a web crawler and was cleaned with Pandas. First, Chinese comments regarding the legal sentencing in of Tangshan attack and Jiang Ge Case were segmented and vectorized. Then, a binary LSTM model was trained and tested. Finally, sentiment analysis results were obtained by analyzing the comments with the LSTM model. The accuracy of the proposed model has reached approximately 92%.
翻译:网络舆论分析由自然语言处理和舆论监督相结合获得,对于监测公众情绪和趋势至关重要,因此,网络舆论分析可以发现和解决潜在和新出现的社会问题,研究的目的是利用长期短期记忆网络模型,在社交媒体审查中分析中国情绪,利用网络爬行器从Sina Weibo获得数据集,并与Pandas一起清理。首先,中国对唐山攻击案和江江盖案的法律判决意见被分割和吸收,然后培训和测试了二进制LSTM模型,最后,通过分析LSTM模型的评论,获得了情绪分析结果,拟议模型的准确性达到约92%。