document: Automatic labeling of multinomial topic models Multinomial distributions over words are frequently used to model topics in text collections . A common , major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic . So far , such labels have been generated manually in a subjective way . In this paper , we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way . We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model . Experiments with user study have been done on two text data sets with different genres . The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models . Our methods are general and can be applied to labeling topics learned through all kinds of topic models such as PLSA , LDA , and their variations .
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