Analysis of Text Emotional Tendency and Prediction of Internet Public Opinion Trend Based on Support Vector Machine Algorithm

Due to the participation of people in online public opinion, users have their own preferences and ideas, which leads to strong chaos in online public opinion. Currently, ML (machine learning) algorithms ignore the chaotic characteristics of online public opinion, so the established model can't fully and accurately describe the changes of online public opinion, and the prediction accuracy needs to be further improved. In this paper, the SVM (support vector machine) algorithm is used to analyze the sentiment tendency of text and predict the trend of Internet public opinion, and the pre-model of sentiment tendency identification and Internet public opinion is established. On the basis of the traditional Gaussian kernel function, we add parameters to increase the displacement change and amplitude adjustment of the kernel function, so that it can accurately control the performance characteristics of the kernel function. The document's overall emotional tendency can be obtained through this accumulation. The results show that the recognition rate of this method is up to 95.828%, and the prediction error is controlled within the effective range (5.028%). This method overcomes the shortcomings of traditional emotional word weighting methods, such as low performance and inability to adapt to multi-viewpoint topics, and can get better recognition accuracy.