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.

中文翻译：

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在线舆论意识的立场检测：概述

立场检测关注用户的深层态度，是了解网络舆情的重要途径。本文概述了姿态检测。首先，我们提出了一个姿态检测的通用框架，并详细介绍了该框架的主要步骤。最先进的姿态检测方法分为三类：基于特征的方法、基于深度学习的方法和基于集成学习的方法。此外，分析了现有方法的优点和局限性。调查结果表明，基于混合神经网络的方法优于其他方法。此外，现有方法还需要更多地关注情感信息、用户交互，并尝试融合更多的外部知识，以帮助提高姿态检测的效果。