论文标题
基于LSTM的社交媒体评论的中国情感分析案例研究
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM
论文作者
论文摘要
网络公众舆论分析是通过自然语言处理（NLP）和公众舆论监督的组合获得的，并且对于监视公众的情绪和趋势至关重要。因此，网络公众舆论分析可以识别并解决潜在和崭露头角的社会问题。这项研究旨在使用长期短期记忆网络（LSTM）模型在社交媒体评论中对中国情绪进行分析。该数据集是使用Web爬网从NINA微博获得的，并用大熊猫清洁。首先，对坦桑袭击和江外案件的法律量刑的评论进行了细分和矢量。然后，对二进制LSTM模型进行了训练和测试。最后，通过使用LSTM模型分析评论来获得情感分析结果。提出的模型的准确性已达到约92％。
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%.
