Public opinion analysis of complex network information of local similarity clustering based on intelligent fuzzy system

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network

The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of the pressing issues facing college administrators right now is finding a method that is both scientific and useful for determining the mental health of college students. In pace with the advancement of Internet technology, the Internet has become an important communication channel for contemporary college students. As one of the main forces in the huge Internet population, college students are at the stage of growing knowledge and being most enthusiastic about new things, and they like to express their opinions and views on study life and social issues and are brave to express their emotions. These subjective text data often contain some affective tendencies and psychological characteristics of college students, and it is beneficial to dig out their affective tendencies to further understand what they think and expect and to grasp their mental health as early as possible. In order to address the issue of assessing the mental health of college students, this study makes an effort to use public opinion data from the university network and suggests a college student sentiment analysis model based on the OCC affective cognitive model and Bi-LSTM neural network. In order to label three different types of positive, negative, and neutral sentiment on the microblog text of college network public opinion, we first design a sentiment rule system based on the OCC affective cognition elicitation mechanism. In order to effectively and automatically identify the sentiment state of college students in the network public opinion, this study uses a Bi-LSTM neural network to classify the preprocessed college network public opinion data. Finally, this study performs comparison experiments to confirm the validity of the Bi-LSTM neural network sentiment recognition algorithm and the accuracy of the OCC sentiment rule labeling system. The findings show that the college student sentiment recognition effect of the model is significantly enhanced when the OCC sentiment rule system is used to label the college network public opinion data set as opposed to the naturally labeled data set. In contrast to SVM and other classification models like CNN and LSTM, the Bi-LSTM neural network-based classification model achieves more satisfactory classification results in the recognition of college opinion sentiment.

Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic

The main purpose of this study is to investigate what topic indicators correlate with public sentiment during “coronavirus disease 2019 (COVID-19) epidemic” and which indicators control the complex networks of the topic indicators. We obtained 68,098 Weibo, categorized them into 11 topic indicators, and grouped these indicators into three dimensions. Then, we constructed the complex networks model of Weibo's topics and examined the key indicators affecting the public's sentiment during the major public emergency. The results showed that “positive emotion” is positively correlated with “recordings of epidemic” and “foreign comparisons,” while “negative emotion” is negatively correlated with “government image,” “recordings of epidemic,” and “asking for help online.” In addition, the two vertexes of “recordings of epidemic” and “foreign comparisons” are the most important “bridges” which connect the government and the public. The “recordings of epidemic” is the main connection “hub” between the government and the media. In other words, the “recordings of epidemic” is the central topic indicator that controls the entire topic network. In conclusion, the government should publish the advance of the events through official media on time and transparent way and create a platform where everyone can speak directly to the government for advice and assistance during a major public emergency in the future.

Public opinion as a means of managing social processes

The relevance of the study is expressed by the improvement and development of tools for managing social processes and public procedures. There are a large number of concepts and theories that explain certain aspects of public opinion: exposure to influence, an impact on people's actions, and its contradictory nature. The main forming factors of individual behavior are the stereotypes and values that are developed within society. In moments of social instability, the problem of governance and expression of public opinion progresses. The purpose of the study is to consider public opinion similarly and to search for patterns of its functioning. At present, there is a lack of an expressly unified model that allows characterizing all the laws of existence and its phenomena. Public opinion is understudied, despite the large volumes of theoretical research. This is explained by the predominance of psychological and sociological scientific concepts in the analysis of public opinion. This study is based on specific methods of conducting various sociological monitoring and developments in the field of methodology. Modern humanitarian knowledge misses certain problems related to the role of public opinion in the context of social processes. The practical significance of this study lies in the investigation and distribution of approaches to the study of public opinion as a means of managing social processes.