TITLE:
Research on College Network Early Warning Based on LSTM
AUTHORS:
Bin Wang, Xiangmeng Fang, Changbin Jiang
KEYWORDS:
College Network Public Opinion, Dynamic Comprehensive Evaluation, Life Cycle Theory, Deep Learning, Early Warning
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.11 No.3,
March
31,
2023
ABSTRACT:
With the development of information technology and the continuous optimization of China’s education system, college students have gradually become the main group in the Internet, and college network public opinion events have received increasing attention. By analyzing the constituent elements of university network public opinion, building an early warning indicator system from three dimensions: subject, carrier, and object, and combining life cycle theory, dynamic comprehensive evaluation method, and short-term memory network, constructing a university network public opinion model. Through empirical analysis of 20 university network public opinion events, the experimental results show that the proposed public opinion model has certain feasibility. Corresponding response strategies have also been proposed for different levels of crisis warning, providing a decision-making basis for relevant departments to guide and manage online public opinion in universities.