Stance detection for online public opinion awareness: An overview
Rong Cao, Xiangyang Luo, Yaoyi Xi, Yaqiong Qiao
Abstract
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.