Stance Classification in Online Debates by Recognizing Users' Intentions

Online debate forums provide a rich collection of differing opinions on various topics. In dual-sided debates, users present their opinions or judge other’s opinions to support their stance. In this paper, we examine the use of users’ intentions and debate structure for stance classification of the debate posts. We propose a domain independent approach to capture users’ intent at sentence level using its dependency parse and sentiWordNet and to build the intention structure of the post to identify its stance. To aid the task of classification, we define the health of the debate structure and show that maximizing its value leads to better stance classification accuracies.