Public Environment Emotion Prediction Model Using LSTM Network

Qiang Zhang, Tianze Gao, Xueyan Liu and Yun Zheng
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Qiang Zhang: College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, Gansu Province, China
Tianze Gao: College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, Gansu Province, China
Xueyan Liu: College of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, Gansu Province, China
Yun Zheng: College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, Gansu Province, China

Sustainability, 2020, vol. 12, issue 4, 1-16

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.

Keywords: public environment emotion; sequentially; long short-term memory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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