Topic Modeling Using Latent Dirichlet Allocation & Multinomial Logistic Regression

Unsupervised categorization for datasets has benefits, but not without a few difficulties. Unsupervised algorithms cluster groups of documents in an unsupervised fashion, and often output findings as vectors containing distributions of words clustered according to their probability of occurring together. Additionally, this technique requires human or domain expert interpretation in order to correctly identify clusters of words as belonging to a certain topic. We propose combining Latent Dirichlet Allocation (LDA) with multi-class Logistic Regression for topic modelling as a multi-step classification process in order to extract and classify topics from unseen texts without relying on human labelling or domain expert interpretation in order to correctly identify clusters of words as belonging to a certain topic. The findings suggest that the two procedures were complementary in terms of identifying textual subjects and overcoming the difficulty of comprehending the array of topics from the output of LDA. Keywords: Natural Language Processing; Topic Modeling; Latent Dirichlet Allocation; Logistic Regression