Stance detection tells whether the expressions of opinion holders are in favor of or against the given objects. To accurately detect stance, the information of the expressed contents must be extracted, alongside a stance match for specific objects. In this study, the Transformer structure and gating attention is applied to specific object stance detection. By effectively utilizing the tag phrase information of the posts and the matching information between posts and objects, which are a result of gating attention mechanism, it delivers a better judgment over the post’s authentic stance regarding the object. Moreover, this approach takes emotional classification as an auxiliary task to fully include emotional information into stance detection for better performance. Experimental results show that the model is superior to the latest deep learning method on the SemEval-2016 dataset.