Social emotion classification of Japanese text information based on SVM and KNN

Abstract

How to model multi-modal data and rich context information under the same model framework has become the key to user sentiment analysis in social media. This research uses SVM and KNN to build models for Japanese social sentiment classification and proposes a topic model for user sentiment analysis based on SVM and KNN. Moreover, taking TE process data as an example, this paper selects radial basis kernel function and grid search method for the construction of SVM classifier. In addition, by introducing the correlation variable between the sentiment of the comment and the sentiment of the tweet, the comment is related to the original tweet. Based on the establishment of the model, this research proposes a parameter estimation algorithm for model solving based on the idea of SVM-KNN, and uses the Twitter real data set as the experimental data set to verify the effectiveness of the user sentiment analysis model proposed in this paper. The research results show that the method proposed in this paper has a certain effect.