The process of identifying and assigning the relationship between two bodies of text is referred to as stance classification. Given a headline and the corresponding body they are compared and their relationship is classified into one of the following two classes - unrelated or related where related is further divided into agree, disagree and discuss. In this article, data is collected from news articles which contains headlines and bodies. We call a headline and the corresponding body as a pair. Deep learning models are applied to these pairs. We applied bidirectional Long Short-Term Memory (LSTM) model and multi-layered perceptron (MLP) model and obtained accuracies of 83.5% and 78% respectively. The accuracy calculation is based on a weighted scheme. The correctly classified unrelated pair has a score of 0.25. A pair correctly classified as related yields a score of one only if the the sub-relationships of agree, disagree and discuss are correctly identified; otherwise, the score is 0.25.
G. Rajendran, Prabaharan Poornachandran, and Dr. Bhadrachalam Chitturi, “Deep learning model on stance classification”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.