Sentiment Evolution in Social Network Based on Joint Pre-training Model

Sentiment analysis is one of the key tasks of natural language understanding. Most of sentiment analysis researches revolve around sentiment classification of subjective texts. However, research in the field of sentiment evolution analysis for complex interactive texts are notable. Sentiment evolution models the dynamics of sentiment orientation over time, it can predict the stage of event development. In this paper, we propose a sentiment evolution method based on a joint model to analyze the dynamics and interactions of individual sentiment on social media such as Weibo. The model contains two modules, sentiment encoder module based on pre-training model and time series prediction module based on Long Short-Term Memory(LSTM). We conducted experiments on real-world datasets which were crawled from Weibo. The experiment demonstrated a case study that analyzed the sentiment dynamics of topics related to COVID-19. Experimental results show that our method achieve an accuracy of 88.0%, which are about 14.7% higher than the existing methods.