Mining ethnic content online with additively regularized topic models

Abstract

Social studies of the Internet have adopte large-scale text mining for unsupervised discovery o topics related to specific subjects. A recently develope approach to topic modeling, additive regularizatio of topic models (ARTM), provides fast inference an more control over the topics with a wide variety o possible regularizers than developing LDA extensions We apply ARTM to mining ethnic-related conten from Russian-language blogosphere, introduce a ne combined regularizer, and compare models derived fro ARTM with LDA. We show with human evaluations tha ARTM is better for mining topics on specific subjects finding more relevant topics of higher or comparabl quality.