Research on Dynamic Political Sentiment Polarity Analysis of Specific Group Twitter Based on Deep Learning Method

Abstract

Abstract The article introduce a method to analyzing the dynamic political sentiment polarity of American politicians’ tweets within a fixed period of time to assist analysts in judging the direction of American politics and the future trend of China-US relations. This research is applied in the field of think tanks or intelligence analysis. The service targets are intelligent analysts, and the data is the tweet text data sent by a specific group in a fixed time period. We propose an architecture that combines multiple deep learning models and use a dedicated tweet data set to construct a specific group to obtain an sentiment polarity multi-classifier, and then introduce the time characteristics of tweets, and finally obtain the dynamic political sentiment polarity of politicians. The US politician tweets data set proposed in this article verifies that the proposed comprehensive architecture is better than traditional deep learning methods in this task, and the accuracy of the classifier verification set reaches 80.66%. According to the sentiment polarity judgments of 20 US governors and senators, the success rate is 75%. The analysis of individual dynamic political sentiment polarity can provide effective help and intelligence support for analysts. The method in this paper effectively uses a variety of deep learning techniques to assist analysts to obtain more accurate dynamic political sentiment polarity from massive Twitter text data.