ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 文本立场检测综述

1. (东北林业大学信息与计算机工程学院 哈尔滨 150040) (yli@nefu.edu.cn)
• 出版日期: 2021-11-01
• 基金资助:
国家自然科学基金青年科学基金项目(61806049)；黑龙江省自然科学基金项目(F2018001)；黑龙江省应用技术研究与开发计划重大项目(GA18B301)

### Survey of Text Stance Detection

Li Yang, Sun Yuqing, Jing Weipeng

1. (College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040)
• Online: 2021-11-01
• Supported by:
This work was supported by the National Natural Science Foundation of China for Young Scientists (61806049), the Natural Science Foundation of Heilongjiang Province (F2018001), and the Major Projects of Applied Technology Research and Development Plan in Heilongjiang Province (GA18B301).

Abstract: Text stance detection is a basic study of text opinion mining, which aims to analyze the stance expressed in the text towards a specific target. Due to the rapid development of the Internet, the discussions of users for public events and consumer products are growing exponentially. The research of text stance detection is of great importance for product marketing and public opinion decision-making. This paper reviews the research of text stance detection from three angles: target type, text granularity and research method. First, from the perspective of target type, this paper focuses on three aspects: single-target stance detection, multi-target stance detection and cross-target stance detection; from the perspective of text granularity, the paper compares different application scenarios and methods of sentence level stance detection, document level stance detection and debate text stance detection; from the perspective of research methods, the paper introduces the traditional machine learning, topic model, deep learning and “two-stage” methods, and points out the advantages and disadvantages of various methods. Then, the evaluation tasks of text stance detection and the open data resources are summarized. Finally, based on the current research, the paper summarizes the application fields and looks forward to the future development trends and challenges of text stance detection.