ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (11): 2538-2557.doi: 10.7544/issn1000-1239.2021.20200518

• 人工智能 • 上一篇    下一篇

文本立场检测综述

李洋,孙宇晴,景维鹏   

  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).

摘要: 文本立场检测是文本意见挖掘领域的基础性研究,旨在分析文本中对特定目标所表现的立场倾向.随着互联网的飞速发展,用户对于公共事件、消费产品等的讨论文本呈指数级增长,文本立场检测研究对产品营销、舆情决策等具有重要意义.从目标类型、文本粒度以及研究方法3个角度对文本立场检测研究工作展开综述.首先,从目标类型角度,围绕单目标、多目标以及跨目标立场检测3个方面梳理了文本立场检测的不同研究任务;从文本粒度角度,对比了句子级、篇章级以及辩论文本立场检测的不同研究场景和方法;从研究方法角度,介绍了基于传统机器学习、主题模型、深度学习以及“2阶段”的方法,并指出各种方法的可取与不足之处.接着,对文本立场检测评测任务以及公开数据资源进行了归纳.最后,立足当前研究形势,总结了文本立场检测研究的应用领域,展望了未来发展趋势以及面临的挑战.

关键词: 文本, 意见挖掘, 立场检测, 目标, 立场

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.

Key words: text, opinion mining, stance detection, target, stance

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