Abstract:Stance detection task aims to automatically determine whether a Weibo text is in favor of the given target, against the given target, or neither. Mining the stance information about a given target is an emerging problem. Based on the success of deep learning in classifying, this study proposed a Bert-Condition-CNN model to predict the stance label. Firstly, noted that the given target may not be present in the Weibo text, so we extracted the topic phrases from Weibo corpus as the given target supplement. Then, we used Bert language model to accept the text representation vector and calculated a Condition matrix whose entries represent the relationship between Weibo text and topic phrases. Finally, a convolutional neural network was utilized to capture the stance features from Condition matrix. Experimental results on NLPCC2016 datasets demonstrate the model has achieved a sound effect of stance detection.