Abstract
The aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.
Highlights
At the moment, of public health incidents are unavoidable for our human society, most notably an outbreak of a novel coronavirus pneumonia (COVID-19) in Wuhan, China, in early 2020
Rao et al proposed a social sentiment monitoring dictionary based on a sentiment dictionary [6]; Tripathy et al combined the naive Bayes (NB) approach, maximum entropy (ME), stochastic gradient descent (SGD) and support vector machine (SVM) machine learning algorithms with n-gram models for sentiment analysis [7]; Jie et al proposed a classification algorithm that combined a dictionary with machine learning [8]; and Ma et al proposed the enhancement of the long short-term memory (LSTM) artificial neural network by integrating both explicit and implicit knowledge, in an approach known as perceptual LSTM [9]
We analyse and predict the evolution of sentiment within public opinion data in an attempt to find the laws underlying the evolution of emotion around large-scale public opinion events and propose an algorithm to measure the emotional value of text based on machine learning
Summary
Of public health incidents are unavoidable for our human society, most notably an outbreak of a novel coronavirus pneumonia (COVID-19) in Wuhan, China, in early 2020. This was a major public health emergency, with the highest speed of transmission, the broadest range of infection, and the highest level of difficulty of. Complex & Intelligent Systems (2021) 7:3165–3178 differences in emotional evolution between Wuhan and rest of China and provide specific measures for public opinion prevention and response to government departments. These content fall into the research category of big data text mining and public sentiment analysis. Experimental results have shown that these methods improved the accuracy of sentiment analysis to a certain extent
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