Sentiment analysis of food safety internet public opinion based on XLNet

Abstract

Internet public opinion sentiment analysis is significant for managing and controlling food safety events. Since emotions can play a decisive role in behavior, netizens’ emotions towards the food safety events will influence their expressions of opinions on the Internet, thereby influencing the development of public opinion on the events. However, few scholars have analyzed the sentiment of Internet public opinion regarding food safety. We employ XLNet, a dynamic text representation method, to build context-dependent word vectors for the distributed representation of Internet public opinion in order to better analyze Internet public opinion on food safety events according to its characteristics. Then, we input the word vectors into Convolutional Neural Networks (CNN) and Bi-directional Long Short-Term Memory (BiLSTM) layers for local semantic features and contextual semantic extraction. Additionally, we introduce an attention mechanism to assign different weights to the features based on their importance before conducting sentiment tendency analysis. The experimental results showed that the average accuracy and Fl values of the sentiment analysis model proposed in this study for Internet public opinion regarding food safety reached 94.12% and 94.61%, respectively, which achieved better results than comparable sentiment analysis models.