Code-switching is commonly used in the free-form text environment, such as social media, and it is especially favored in emotion expressions. Emotions in code-switching texts differ from monolingual texts in that they can be expressed in either monolingual or bilingual forms. In this paper, we first utilize two kinds of knowledge, i.e. bilingual and sentimental information to bridge the gap between different languages. Moreover, we use a term-document bipartite graph to incorporate both bilingual and sentimental information, and propose a label propagation based approach to learn and predict in the bipartite graph. Empirical studies demonstrate the effectiveness of our proposed approach in detecting emotion in code-switching texts.