关键词: 舆情反转预测, 事件特征预训练, 舆情演化, 自然语言处理, Transformer

Abstract: In current research on public opinion prediction, event representation has a certain degree of subjectivity and stationarity, and does not fully express the dynamic and evolutionary nature of event evolution. Many features need to be obtained through analyzing the complete process of event development, resulting in the constructed prediction model not being able to achieve the warning purpose before the occurrence of public opinion phenomena. This paper constructs an event pre-training model to automatically generate event feature vector based on comments data, and it is used to train downstream public opinion reversal prediction models. By combining subjective comments and temporal information of events, the problem of generating abstract event feature vectors is transformed into a natural language preprocessing problem by constructing comment words, event word vectors, event words, and event sentences. Based on the Transformer structure, a new modeling method is proposed to achieve automatic generation of event feature vectors and prediction of public opinion reversal. When the model proposed in this paper is used for downstream tasks of predicting public opinion reversal, the prediction rate of reversal events in the test set reaches 100%, achieving the goal of predicting reversal phenomena before the reversal point. At the same time, the prediction model can also accurately predict the generation of event sentences for the next day. In the n-fold cross validation of the test set, only 11% of the events have showed prediction errors, providing data and methodological basis for studying issues related to public opinion evolution.

Key words: public opinion reversal prediction, event feature pre-training, public opinion evolution, natural language processing, Transformer