Additive regularization for topic modeling of social media communities

The task of modeling communities (groups) of users in social media is relevant in the framework of information support for decision-making at different levels of government. For automated extraction of the meaning of textual and related information, topic modeling methods are used. This article presents the experience of improving the results of social networks communities topic modeling using the Additive Regularization for Topic Modeling (ARTM). The improvement of the results is achieved through the use of basic regularizers available in the open-source software BigARTM. Topic models obtained using regularized ARTM are compared with topic models obtained by Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis. The experiments were carried out on a dataset, containing preprocessed texts of posts from communities of an online social network. In particular, the quality of topic models in terms of coherence, the purity of topics, and the sparsity of the distribution matrices are compared. Disadvantages of coherence metrics for assessing the quality of topic models obtained using the ARTM method are discussed. Additional metrics are proposed that can be used for assessing the quality of topic models. Conclusions are drawn about the suitability of the ARTM approach for modeling communities of online social networks. The results of this work can be applied in the development of information and analytical systems for supporting the management of regional development.