Text classification with a few labeled samples based on latent Dirichlet allocation using PTM.

For the issue that it is only a few labeled samples in really text classification environment which will affect the classification accuracy, this paper proposed a classification algorithm based on latent Dirichlet allocation using probabilistic topic model. Firstly, it used standard term frequency-inverse document frequency function to represent each document into term weight vector. Then, it used probabilistic topic model as pretreatment to simplify the document, and done term extraction from document. Finally, it used latent Dirichlet allocation model to do relational learning and used classification based on graph to finish classification. The effectiveness of proposed method has been verified by experiments on common resource library Reu-ters-21578. Experimental results show that proposed method has higher classification accuracy than support vector machine which has well classification effect in most cases. [ABSTRACT FROM AUTHOR]