Stance Classification with Target-specific Neural Attention

Stance Classification with Target-specific Neural Attention

Jiachen Du, Ruifeng Xu, Yulan He, Lin Gui

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3988-3994. https://doi.org/10.24963/ijcai.2017/557

Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-the-art performance.
Keywords:
Natural Language Processing: Natural Language Processing
Natural Language Processing: Sentiment Analysis and Text Mining
Machine Learning: Deep Learning