Community detection is a pivotal task in data mining, and users' emotional behaviors have an important impact on today's society. So it is very significant for society management or marketing strategies to detect emotional communities in social networks. Based on the emotional homophily of users in social networks, it could confirm that users would like to gather together to form communities according to emotional similarity. This paper exploits multivariate emotional behaviors of users to measure users' emotional similarity, then takes advantage of users' emotional similarity as edge weight to remodel an emotional network and detect communities. The detailed process of detecting emotional communities is as follows: 1) an emotional network is constructed and emotional homophily in experimental dataset is verified; 2) both CNM and BGLL algorithms are employed to detect emotional communities in emotional network, and emotional characters of each community are analyzed; 3) in order to verify the superiority of emotional network for detecting emotional communities, 1 unweighted network and 3 other weighted and undirected networks are constructed as comparison. Comparison experiments indicate that the emotional network is more suitable for detecting emotional communities, the users' emotional behaviors are more similar and denser in identical communities of emotional network than the contrastive networks' communities.