Stance detection aims to automatically classify the stance of a text’s author towards a given target. Some previous studies have used Wikipedia to infuse background knowledge about the target into stance detection, and others have improved the performance of stance detection by using sentiment information of the text. However, there's yet to be a study that utilizes both types of information simultaneously. This paper proposes a BERT-based stance detection model that encodes information from Wikipedia on the target and the sentiment of the text. The experiments on the P-Stance dataset shows that our proposed model outperforms previous models