Topic-Feature Lattices Construction and Visualization for Dynamic Topic Number

Abstract The topic recognition for dynamic topic number can realize the dynamic update of super parameters, and obtain the probability distribution of dynamic topics in time dimension, which helps to clear the understanding and tracking of convection text data. However, the current topic recognition model tends to be based on a fixed number of topics K and lacks multi-granularity analysis of subject knowledge. Therefore, it is impossible to deeply perceive the dynamic change of the topic in the time series. By introducing a novel approach on the basis of Infinite Latent Dirichlet allocation model, a topic feature lattice under the dynamic topic number is constructed. In the model, documents, topics and vocabularies are jointly modeled to generate two probability distribution matrices: Documents-topics and topic-feature words. Afterwards, the association intensity is computed between the topic and its feature vocabulary to establish the topic formal context matrix. Finally, the topic feature is induced according to the formal concept analysis (FCA) theory. The topic feature lattice under dynamic topic number (TFL_DTN) model is validated on the real dataset by comparing with the mainstream methods. Experiments show that this model is more in line with actual needs, and achieves better results in semi-automatic modeling of topic visualization analysis.