Self-adaptive topic model: A solution to the problem of rich topics get richer

The problem of rich topics get richer (RTGR) is popular to the topic models, which will bring the wrong topic distribution if the distributing process has not been intervened. In standard LDA (Latent Dirichlet Allocation) model, each word in all the documents has the same statistical ability. In fact, the words have different impact towards different topics. Under the guidance of this thought, we extend ILDA (Infinite LDA) by considering the bias role of words to divide the topics. We propose a self-adaptive topic model to overcome the RTGR problem specifically. The model proposed in this paper is adapted to three questions: (1) the topic number is changeable with the collection of the documents, which is suitable for the dynamic data; (2) the words have discriminating attributes to topic distribution; (3) a self-adaptive method is used to realize the automatic re-sampling. To verify our model, we design a topic evolution analysis system which can realize the following functions: the topic classification in each cycle, the topic correlation in the adjacent cycles and the strength calculation of the sub topics in the order. The experiment both on NIPS corpus and our self-built news collections showed that the system could meet the given demand, the result was feasible.