Random set to interpret topic models in terms of ontology concepts

Abstract

Topic modelling is a popular technique in text mining. However, discovered topic models are difficult to interpret due to incoherence and lack of background context. Many applications require an accurate interpretation of topic models so that both users and machines can use them effectively. Taking the advantage of random set and a domain ontology, this research can interpret the topic models. The interpretation is evaluated by comparing it with different baseline models on two standard datasets. The results show that the performance of the interpretation is significantly better than baseline models.