•We study the problem of social emotion mining of online users in the news domain.•Two models are proposed and devised for modeling topics and emotions jointly.•The influence of topic numbers on different models is analyzed.•The effect of hyperparameter on the proposed models is studied.•The samples of social emotion lexicon are investigated qualitatively. The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers’ emotions evoked by social media materials. Compared to the classical sentiment analysis from writers’ perspective, sentiment analysis of readers is sometimes more meaningful in social media. We propose two sentiment topic models to associate latent topics with evoked emotions of readers. The first model which is an extension of the existing Supervised Topic Model, generates a set of topics from words firstly, followed by sampling emotions from each topic. The second model generates topics from social emotions directly. Both models can be applied to social emotion classification and generate social emotion lexicons. Evaluation on social emotion classification verifies the effectiveness of the proposed models. The generated social emotion lexicon samples further show that our models can discover meaningful latent topics exhibiting emotion focus.