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Latent Dirichlet allocation (LDA) is a topic model that has been applied to various fields, including user profiling and event summarization on Twitter. When LDA is applied to tweet collections, it generally treats all aggregated tweets of a user as a single document. Twitter-LDA, which assumes a single tweet consists of a single topic, has been proposed and has shown that it is superior in topic semantic coherence. However, Twitter-LDA is not capable of online inference. In this study, we extend Twitter-LDA in the following two ways. First, we model the generation process of tweets more accurately by estimating the ratio between topic words and general words for each user. Second, we enable it to estimate the dynamics of user interests and topic trends online based on the topic tracking model (TTM), which models consumer purchase behaviors.

Online topic model for Twitter considering dynamics of user interests and topic trends