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Topic modelling has been popularly used to discover latent topics from text documents. Most existing models work on individual words. That is, they treat each topic as a distribution over words. However, using only individual words has several shortcomings. First, it increases the co-occurrences of words which may be incorrect because a phrase with two words is not equivalent to two separate words. These extra and often incorrect co-occurrences result in poorer output topics. A multi-word phrase should be treated as one term by itself. Second, individual words are often difficult to use in practice because the meaning of a word in a phrase and the meaning of a word in isolation can be quite different. Third, topics as a list of individual words are also difficult to understand by users who are not domain experts and do not have any knowledge of topic models. In this paper, we aim to solve these problems by considering phrases in their natural form. One simple way to include phrases in topic modelling is to treat each phrase as a single term. However, this method is not ideal because the meaning of a phrase is often related to its composite words. That information is lost. This paper proposes to use the generalized Pólya Urn (GPU) model to solve the problem, which gives superior results. GPU enables the connection of a phrase with its content words naturally. Our experimental results using 32 review datasets show that the proposed approach is highly effective.