Research on trend prediction of time-coded LSTM based public opinion hot spots in universities

Abstract

With the development of Internet technology, network public opinion hot information can be quickly spread in a short time.Predicting the development trend of public opinion hot spots is helpful to the analysis and management of college students’ ideological health, and it is also an important issue in the field of network public opinion information research.Aiming at the public opinion information text in microblog, the hot spots trend prediction model of universities based on time-coded long short-term memory (LSTM) was constructed.Compared with the prediction effect of support vector machine, and recurrent neural network through experiments, the superiority of time-coded LSTM was verified.Finally, the prediction effect of time-coded LSTM was evaluated by using the real-time public opinion events of colleges and universities in microblogs, and the evaluation parameters were dynamically adjusted to optimize the performance of the evaluation, and the prediction effect was improved significantly.

Keywords

• LSTM;hot spots prediction;public opinion in universities;time series data;time-coded