An Empirical Study on Chinese Microblog Stance Detection using Supervised and Semi-supervised Machine Learning Methods

摘要：

　　Nowadays,more and more people are willing to express their opinions and attitudes in the microblog platform.Stance detection refers to the task that judging whether the author of the text is in favor of or against the given target.Most of the existing literature are for the debates or online conversations,which have adequate context for inferring the authors stances.However,for detecting the stance in microblogs,we have to figure out the stance of the author only based on the unique and separate microblog,which sets new obstacles for this task.In this paper,we conduct a comprehensive empirical study on microblog stance detection using supervised and semi-supervised machine learning methods.Different unbalanced data processing strategies and classifiers,such as Linear SVM,Naive Bayes and Random Forest,are compared using NLPCC2016 Stance Detection Evaluation Task dataset.Experiment results show that the method based on ensemble learning and SMOTE2 unbalanced processing with sentiment word features outperforms the best submission result in NLPCC2016 Evaluation Task.

关键词： stance detection Chinese microblog sentiment analysis supervised learning semi-supervised learning unbalanced classification

作者: Liran Liu Shi Feng Daling Wang Yifei Zhang

作者单位: School of Computer Science and Engineering,Northeastern University,China School of Computer Science and Engineering,Northeastern University,China;Key Laboratory of Medical I

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-12

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）