Artificial Intelligence Technology-Based Semantic Sentiment Analysis on Network Public Opinion Texts

Abstract

Considering that the current social network text analysis works poorly in accurate and effective sentiment prediction and management, a deep learning (D-L)-based text sentiment analysis method is proposed for the big data environment. First, the autoregressive language model mode XLNet is used to capture bidirectional text information and a sentiment analysis model XLNet-Multi-Attention-BiGRU. Then, considering the context information of social network texts, the defect of traditional GRU units only reading texts in order is overcome by introducing a BiGRU model to extract features in both directions. Finally, a multi-headed attention layer is added between the BiGRU and CRF layers to better capture the key information in the sentence by integrating multiple single-head attention. The results show that the precision, recall, and F1 value of the method proposed in this paper are the largest, with the highest reaching 92.64%, 92.32%, and 91.25%, respectively, which are 12.40%, 10.17%, and 9.63% higher than the maximum values of the other three methods, respectively.

A Fine-Grained Sentiment Analysis Method Using Transformer for Weibo Comment Text

“…Unstructured text data, like public opinion expressed through social media and surveys, is gaining importance as a valuable information source. For public stability, government departments should actively monitor public opinion, understand citizen sentiment, and keep them informed through transparent information flow (Fan, 2023). Public opinion control is not only a matter for relevant staff but also one that concerns the vital interests of all citizens (Djaballah et al, 2021;Li et al, 2021;Tamil et al, 2022;Zhang & Cui, 2023;Zhang et al, 2020).…”