Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (5): 1402-1406.DOI: 10.11772/j.issn.1001-9081.2017.05.1402

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Stance detection method based on entity-emotion evolution belief net

LU Ling, YANG Wu, LIU Xu, LI Yan   

  1. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400050, China
  • Received:2016-09-30 Revised:2016-12-22 Online:2017-05-10 Published:2017-05-16
  • Supported by:
    This work is partially supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1500903)。


卢玲, 杨武, 刘旭, 李言   

  1. 重庆理工大学 计算机科学与工程学院, 重庆 400050
  • 通讯作者: 杨武
  • 作者简介:卢玲(1975-),女,重庆人,副教授,硕士,CCF会员,主要研究方向:机器学习、信息检索;杨武(1965-),男,重庆人,教授,CCF会员,主要研究方向:信息检索、机器学习;刘旭(1997-),男,河北石家庄人,CCF会员,主要研究方向:机器学习、并行计算;李言(1996-),男,重庆人,CCF会员,主要研究方向:机器学习、信息检索。
  • 基金资助:

Abstract: To deal with the problem of stance detection of Chinese social network reviews which lack theme or emotion features, a method of stance detection based on entity-emotion evolution Bayesian belief net was proposed in this paper. Firstly, three types of domain dependent entities, including noun, verb-object phrase and verb-noun compound attributive centered structure were extracted. The domain-related emotion features were extracted, and the variable correlation strength was used as a constraint on the learning of the network structure. Then the 2-dependence Bayesian network classifier was constructed to describe the dependence of entity, stance and emotion features. The stance of reviews was deducted from combination condition of entities and emotion features. Experiments were tested on Natural Language Processing & Chinese Computing 2016 (NLP&CC2016). The experimental results show that the average micro-F reaches 70.8%, and average precision of FAVOR and AGAINST increases by 4.1 percentage points and 3.1 percentage points over Bayesian network classification method with emotion features only respectively. The average micro-F on 5 target data sets of evaluation reaches 62.3%, which exceeds average level of the evaluation.

Key words: stance detection, Bayesian Network (BN), domain dependent entity, network structure learning, attributive-centered structure

摘要: 社交网络评论文本存在评论主题缺失或情感特征缺失的问题,无法保证观点检测的性能,对此提出了建立实体情感演化贝叶斯置信网的方法。通过提取名词、动宾短语、动名词复合型定中结构短语三种域相关实体,提取域相关情感特征,用可变关联强度作为网络结构学习的约束条件,建立2阶依赖扩展贝叶斯网络,刻画实体、观点及情感特征的依赖关系,再通过实体及情感特征对观点极性进行推断。实验在自然语言处理与中文计算2016(NLP&CC2016)评测训练数据集的F值平均达70.8%,FAVOR和AGAINST两类正确率分别比仅包含情感特征的贝叶斯网络分类方法提高4.1个百分点和3.1个百分点。在5个Target评论测试集上的平均Micro-F为62.3%,优于该评测的平均水平。

关键词: 观点检测, 贝叶斯网络, 域相关实体, 网络结构学习, 定中结构

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