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In many real-world topic detection tasks, the process of the topic detection is often interactive, which means the users are likely to interfere the reason process by expressing their preferences. We proposed an algorithm, iOLDA, and the software framework for interactive topic evolution pattern detection based on Latent Dirichlet Allocation (LDA). To abate those topics not interested or related, it allows the users to add supervised information by adjusting the posterior topic-word distributions at the end of each iteration, which may influence the inference process of the next iteration. Experiments are conducted both on English and Chinese corpus and the results show that the extracted topics capture meaningful themes in the data, and the proposed interaction policies can help to discover better topics.