An empirical study on chinese microblog stance detection using supervised and semi-supervised machine learning methods

Abstract

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 NLPCC 2016 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 NLPCC 2016 Evaluation Task.