基于RNN-LSTM新冠肺炎疫情下的微博舆情分析

Public Opinion Analysis on Weibo Based on RNN-LSTM in COVID-19

目前微博已经成为网络舆论传播和挖掘民意的重要平台,为分析疫情事件对网民情绪的影响,科学高效地做好防控宣传和舆情引导工作,为此融合不同的深度学习方法对2020年初发生的新冠疫情的微博评论进行情感分析。提出一种基于RNN(Recursive Neural Network)和LSTM(Long Short-Term Memory)混合模型并在嵌入层中使用FastText词向量表示方法,以降低词向量中的噪声数据,从而获得语义丰富且噪声少的高质量词向量,并与朴素贝叶斯、支持向量机、RNN、LSTM多种情感分析方法进行比较。结果表明,所提出的情感分析模型正确率达到了98.71%,证明了该模型能有效提升情感分析正确率。

In recent years,microblog has become an important platform for Internet public opinion dissemination and public opinion mining.In order to analyze the impact of epidemic events on Netizens’emotions,we should do a good job in prevention and control publicity and public opinion guidance scientifically and efficiently.Therefore,we integrate different deep learning methods to conduct emotional analysis of microblog comments on the COVID-19 outbreak at the end of 2020.A hybrid model based on RNN(Recursive Neural Network)and LSTM(Long Short-Term Memory)and using the FastText word vector representation in the embedding layer is proposed to reduce the noise data in the word vectors and thus obtain high-quality word vectors with semantically rich and less noise.Training on Weibo corpora and compared with Bayesian and Support Vector Machine,RNN,LSTM multiple methods,the results show that the accuracy of the emotion analysis model proposed in this paper reaches 98.71%,which proves that the model can effectively improve the accuracy of emotion analysis.