Online Public Opinion Analysis Model Based on Long Short-term Memory Network and Expectation Maximization

Abstract

Abstract With the rapid dissemination of online public opinion, its emotions are easily transmitted to the general public. It possesses a certain level of social mobilization capacity and can impact the stability of society. To characterize the sentiment trend of social network information and determine its influence, we have proposed a method based on long short-term memory (LSTM) networks and expectation maximization(EM). This model employs a long short-term memory network for data training, obtaining the number of positive public opinions through word-to-word matching. Based on the expectation-maximization method and Jensen’s inequality, the lower bound of the maximum likelihood function is iteratively computed, ultimately achieving convergence of this likelihood function. This convergence value is then used for sentiment analysis. Our study utilizes 10,000 valid pieces of data from the Sina microblog as experimental data. By comparing our model with the K-MEANS model and the EM model, the results indicate significant improvements in the accuracy and convergence of our model. Our research discovers that the influence of public opinion increases as the compensation value for adoption rises, and the probability of public opinion generation gradually increases with the length of user registration years, eventually slowing down.