Network Sentiment Analysis of College Students in Different Epidemic Stages Based on Text Clustering

In order to analyze the evolution trend of public opinion in emergencies and explore its evolution law, this paper constructs a network sentiment analysis model based on text clustering, where the emotion analysis part is based on the pretraining BERT model and BiGRU model, in which BERT is used as the word embedding model to extract the feature vector of emotional text and BiGRU is used to extract the context of the text feature vector to accurately identify the sentiment polarity of public opinion data. In addition, the K-means clustering algorithm and Kolmogorov-Smirnov Z test were used to divide the different epidemic stages. Compared with other methods, the model proposed in this paper has a great degree of improvement in accuracy, recall, and F1 score index, which provides an opportunity reference and effective detection means for schools at all levels to carry out timely mental health education and psychological intervention for students.