Learning Stance Classification with Recurrent Neural Capsule Network

Stance classification is a natural language processing (NLP) task to detect author’s stance when give a specific target and context, which can be applied in online debating forum, e.g., Twitter, Weibo, etc. In this paper, we present a novel target orientation recurrent neural capsule network, called TRNN-Capsule to solve the problem. In TRNN-Capsule, the target and context are both encoded by leveraging a bidirectional LSTM model. Then, capsule blocks are appended to produce the final classification outcome. Experiments on two benchmark data sets are conducted and the results show that the proposed TRNN-Capsule outperforms state-of-the-art competitors for the stance classification task.