Public Environment Emotion Prediction Model Using LSTM Network

Abstract

Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile “impression ecology” platform to collect time spans in five cities in Lanzhou for one year. In addition, a parameter optimization algorithm, WOA (Whale Optimization Algorithm), is introduced on the basis of the prediction method. It is expected to predict the public environmental sentiment more accurately while predicting the atmospheric environment. This paper compares the decision performance of LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) models on the public environment emotional level through experiments, and uses a variety of error assessment methods to quantitatively analyze the prediction results, verifying the LSTM’s performance in prediction performance and level decision-making effectiveness and robustness.