Online Latent Dirichlet Allocation Model Based on Sentiment Polarity Time Series

Abstract

The Product Sensitive Online Dirichlet Allocation model (PSOLDA) proposed in this paper mainly uses the sentiment polarity of topic words in the review text to improve the accuracy of topic evolution. First, we use Latent Dirichlet Allocation (LDA) to obtain the distribution of topic words in the current time window. Second, the word2vec word vector is used as auxiliary information to determine the sentiment polarity and obtain the sentiment polarity distribution of the current topic. Finally, the sentiment polarity changes of the topics in the previous and next time window are mapped to the sentiment factors, and the distribution of topic words in the next time window is controlled through them. The experimental results show that the PSOLDA model decreases the probability distribution by 0.160 1, while Online Twitter LDA only increases by 0.069 9. The topic evolution method that integrates the sentimental information of topic words proposed in this paper is better than the traditional model.

Short text sentiment analysis combining sentiment lexicon and graph convolutional networks

“…Regarding sentiment analysis methods, they are mainly divided into three categories: those based on sentiment lexicons [1], those based on machine learning [2], and those based on deep learning [3]. Sentiment analysis methods based on sentiment lexicons primarily utilize pre-built sentiment lexicons to determine the sentiment orientation of words in the text based on the sentiment values of the words in the lexicon.…”