Document classification by topic labeling

An extension to the Latent Dirichlet Allocation (LDA) based document classification algorithm based on the combination of Expectation-Maximization (EM) algorithm and a naive Bayes classifier is presented.

Abstract

In this paper, we propose Latent Dirichlet Allocation (LDA) [1] based document classification algorithm which does not require any labeled dataset. In our algorithm, we construct a topic model using LDA, assign one topic to one of the class labels, aggregate all the same class label topics into a single topic using the aggregation property of the Dirichlet distribution and then automatically assign a class label to each unlabeled document depending on its "closeness" to one of the aggregated topics.