Traditional topic models, like LDA and PLSA, have been efficiently extended to capture further aspects of text in addition to the latent topics (e.g., time evolution, sentiment etc.). In this paper, we discuss the issue of joint topicsentiment modeling. We propose a novel topic model for topic-specific sentiment modeling from text and we derive an inference algorithm based on the Gibbs sampling process. We also propose a method for automatically setting the model parameters. The experiments performed on two review datasets show that our model outperforms other stateof-the-art models, in particular for sentiment prediction at the topic level.

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Mining medical data has significantly gained interest in the recent years thanks to the advances in data mining and machine learning fields. In this work, we focus on a challenging issue in medical data mining: automatic diagnosis code assignment to discharge summaries, i.e., characterizing patient's hospital stay (diseases, symptoms, treatments, etc.) with a set of codes usually derived from the International Classification of Diseases (ICD). We cast the problem as a machine learning task and we experiment some recent approaches based on the probabilistic topic models. We demonstrate the efficiency of these models in terms of high predictive scores and ease of result interpretation. As such, we show how topic models enable gaining insights into this field and provide new research opportunities for possible improvements.