Textual Information Classification of Campus Network Public Opinion Based on BILSTM and ARIMA

Abstract

To address the problem that it is difficult for traditional opinion analysis models to accurately analyze textual information of campus online public opinion in various formats, a deep learning-based online opinion analysis method is proposed by combining BILSTM and ARIMA models. By using BILSTM sentiment classification model to predict and analyze the text data of campus online public opinion, the sentiment polarity of online public opinion information was well predicted, and the trend prediction of online public opinion was completed by combining ARIMA model difference and temporal preprocessing BILSTM with accuracy values as the original sequence. The simulation results show that the proposed method can better achieve the sentiment prediction of campus online opinion event texts and can predict the general trend of campus online opinion development. The prediction results can well reflect the actual online public opinion and have better prediction accuracy compared with CNN or LSTM models, which can reach more than 80%.