The predicting public sentiment evolution on public emergencies under deep learning and internet of things

With the Internet's rapid development and the increasing amount of netizens, social contradictions frequently manifest over the Internet. Public emergencies develop and spread constantly online. Thus, it is of great significance to reasonably address the Online Public Sentiment (OPS) in the current critical stage of social transformation. The aim is to create a safe and credible network environment and realize the modern transformation of the dynamic evolution of OPS in public emergencies. Firstly, this paper expounds on the blocking process of the OPS evolution on public emergencies according to the Internet of Things-native big data. Then, it discusses the algorithm process of the Long Short-Term Memory (LSTM) Neural Network (NN) model. Further, it optimizes the LSTM NN model using the Adaptive Momentum Estimation (Adam). Finally, it simulates and predicts the OPS evolution using Artificial Intelligence technology and big data. The results show that the Adam-optimized LSTM NN model can predict the hotness of OPS in the dynamic evolution with high prediction accuracy. In predicting OPS evolution, the Mean Relative Errors (MRE) of the proposed Adam-LSTM, LSTM, and Backpropagation NN models are 0.06, 0.10, and 0.14, respectively. The proposed Adam-LSTM model presents the least MRE on the hotness of OPS. The relevant governments can refer to model-predicted OPS evolution to control public emergencies and OPS through the IoT. Therefore, the proposed Adam-LSTM model is feasible for predicting the OPS hotness. The finding has particular research significance for employing the LSTM model under the IoT in predicting the OPS evolution in public emergencies. Lastly, the OPS on public emergencies can be effectively guided thanks to the proposed Adam-LSTM prediction model and time nodes.