Microblogging is a popular way for people to post, share, and seek information due to its convenience and low cost. However, it also facilitates the generation and propagation of fake news, which could cause detrimental societal consequences. Detecting fake news on microblogs is important for societal good. Emotion is considered a significant indicator in many fake news detection studies, and most of them utilize emotion mainly through users stances or simple statistical emotional features. In reality, the publishers typically post either a piece of news with intense emotion which could easily resonate with the crowd, or a controversial statement unemotionally aiming to evoke intense emotion among the users. However, existing studies that exploiting the emotion information from both news content and user comments corporately is ignored. Therefore, in this paper, we study the novel problem of learning dual emotion for fake news detection. We propose a new Dual Emotion-based fAke News detection framework (DEAN), which can i) learn content- and comment- emotion representations for publishers and users respectively; and ii) exploit the dual emotion representations simultaneously for fake news detection. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.