The Application of Support Vector Machine (SVM) on the Sentiment Analysis of Internet Posts

Abstract

At present, in the mainstream sentiment analysis methods represented by Support Vector Machine, the vocabulary and the latent semantic information contained in the text cannot be well considered, and sentiment analysis of text is overly dependent on the statistics of sentiment words. In this paper, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived by the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information containing the probability features can be used as the classification features, the effect of classification for support vector machine can be improved, and the problem of not considering the latent semantic features in text sentiment analysis is solved. The results show that compared with the comparison method, the effect of the method proposed in this paper is obviously improved.