Topic-based stance mining for social media texts

Abstract

Recent techniques of opinion mining have succeeded in analyzing sentiment on the social media, but processing the skewed data or data with few labels about political or social issues remains tough. In this paper, we introduce a two-step approach that starts from only five seed words for detecting the stance of Facebook posts toward the anti-reconstruction of the nuclear power plant. First, InterestFinder, which detects interest words, is adopted to filter out irrelevant documents. Second, we employ machine learning methods including SVM and co-training, and also a compositional sentiment scoring tool CopeOpi to determine the stance of each relevant post. Experimental results show that when applying the proposed transition process, CopeOpi outperforms the other machine learning methods. The best precision scores of predicting three stance categories (i. e., supportive, neutral and unsupportive) are 94. 62%, 88. 86% and 10. 47%, respectively, which concludes that the proposed approach can capture the sentiment of documents from lack-of-label, skewed data.