Intuitive Topic Discovery by Incorporating Word-Pair´s Connection Into LDA

We demonstrate a generative model that incorporates word-pair connection into the smoothed LDA model to intuitively discover people´s wish related activities. The widely used model, LDA topic model, generally generates clusters in the form of separate words. However, this form is not intuitive enough to express people´s activities. Therefore, we consider the word-pairs led by verbs can better describe users´ intentions and activities, and we prefer to present this collocation under topics as the clustering results. We mathematically present the relatedness between verbs and non-verb words through association rule, and build the physical connection of word-pairs and possible topics. By incorporating the connection lattice into the smoothed LDA, the word-pair LDA model is created. In the experiments, Twitter posts about “new year´s resolutions” were chosen as the data source. The results show that the proposed model performs well on perplexity, and presents excellent intuitive character.