Labeled-LDA Text Classification Algorithm Based on Graph Model for“Central Topic Oblivion Problem”
Guobing Li · 2014
Latent Dirichlet Allocation(LDA)is an unsupervised topic model used to mining potential topic information from the corpus.Labeled-LDA as a mutation of LDA can be used to do multi-classification on labeled documents,which establishes the one-to-one mapping from topic to label and learns the relationship between words and labels.Recently, the application of graph model has obtained good results in text mining,which provides a new way to analyze semantics of documents.This paper proposed a new method combining complex network theory and Labeled-LDA to do text classification.The experimental results show that our new method gets an improvement according to Macro_F1compared to the traditional LDA model.