Stance detection for online public opinion awareness: An overview
Rong Cao, Xiangyang Luo, Yaoyi Xi, Yaqiong Qiao · International Journal of Intelligent Systems · 2022
Stance detection, which focuses on users' deep attitudes, is an important way to understand the online public opinion. This paper presents an overview of stance detection. First, we present a general framework for stance detection, and the main steps of the framework are introduced in detail. The state-of-the-art stance detection methods are categorized into three classes: feature-based methods, deep learning-based methods, and ensemble learning-based methods. Moreover, the advantages and limitations of the existing methods are analyzed. The survey findings show that hybrid-neural network-based methods are superior to the other methods. In addition, existing methods still need to pay more attention to the sentiment information, user-interaction, and attempt to merge more external knowledge to help improve the effect of stance detection.