Mining Ethnic Content Online with Additively Regularized Topic Models

Murat Apishev, Sergei Koltcov, Olessia Koltsova, Sergey Nikolenko, Konstantin Vorontsov


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


Topic modeling, additive regularization of topic models, computational social science

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