Topics modeling based on selective Zipf distribution

Automatically mining topics out of text corpus becomes an important fundament of many topic analysis tasks, such as opinion recognition, Web content classification, etc. Although large amount of topic models and topic mining methods have been proposed for different purposes and shown success in dealing with topic analysis tasks, it is desired to create accurate models or mining algorithms for many applications. A general criteria based on Zipf fitness quantity computation is proposed to determine whether a topic description is well-form or not. Based on the quantity definition, the popular Dirichlet prior on multinomial parameters is found that it cannot always produce well-form topic descriptions. Hence, topics modeling based on LDA with selective Zipf documents as training dataset is proposed to improve the quality in generation of topics description. Experiments on two standard text corpuses, i.e. AP dataset and Reuters-21578, show that the modeling method based on selective Zipf distribution can achieve better perplexity, which means better ability in predicting topics. While a test of topics extraction on a collection of news documents about recent financial crisis shows that the description key words in topics are more meaningful and reasonable than that of tradition topic mining method.