The microblog sentiment analysis based on latent dirichlet allocation and deep learning approaches

To study the application of convolutional neural networks (CNN) in microblog sentiment analysis, a microblog sentiment dictionary is established first. Then, latent Dirichlet allocation (LDA) is proposed for user forwarding sentiment analysis. The sentiment analysis models of CNN and long short-term memory network (LSTM) are established. Experiments are conducted to verify the application effect. The main contributions of this work encompass the establishment of a sentiment lexicon for Weibo, the optimization of two sentiment analysis models, namely CNN and LSTM, as well as the comparison and analysis of the performance of three sentiment analysis approaches: CNN, LSTM, and LDA. The research findings indicate that the CNN model achieves a prediction accuracy of 78.6% and an actual output precision of 79.3%, while the LSTM model attains a prediction accuracy of 83.9% and an actual output precision of 84.9%. The three analysis models all have high sentiment analysis accuracy. Among them, LDA analysis model has the advantages of universality and irreplaceable in text classification, while LSTM analysis model has relatively higher accuracy in sentiment analysis of users forwarding microblog. In short, each sentiment analysis model has its own strengths, and reasonable allocation and use can better classify microblog sentiment.