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%.

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

“…Because the public opinion environment of the social networking platform is more open, free, and private than that of real society, and because public publishing and obtaining information in the network is more convenient and faster than exploring, publicizing, or querying information in the real world, more and more people pay attention to daily hot spots or public events through the Internet [1][2][3][4][5]. When people fnd the information they are interested in in the process of browsing the Internet, many people will express their own opinions and opinions on the content of the information, and the information containing the emotional tendencies and subjective attitudes of netizens constitutes a new network public opinion information [6][7][8][9]. By analyzing some network public opinion with a strong emotional attitude, we can fnd that there is not only a lot of positive public opinion information with healthy, positive, and positive energy but also some false, pessimistic, and reactionary negative opinion information.…”