RusLICA: A Russian-Language Platform for Automated Linguistic Inquiry and Category Analysis

Defining psycholinguistic characteristics in written texts is a task gaining increasing attention from researchers. One of the most widely used tools in the current field is Linguistic Inquiry and Word Count (LIWC) that originally was developed to analyze English texts and translated into multiple languages. Our approach offers the adaptation of LIWC methodology for the Russian language, considering its grammatical and cultural specificities. The suggested approach comprises 96 categories, integrating syntactic, morphological, lexical, general statistical features, and results of predictions obtained using pre-trained language models (LMs) for text analysis. Rather than applying direct translation to existing thesauri, we built the dictionary specifically for the Russian language based on the content from several lexicographic resources, semantic dictionaries and corpora. The paper describes the process of mapping lemmas to 42 psycholinguistic categories and the implementation of the analyzer as part of RusLICA web service.

Applying LLM and Topic Modelling in Psychotherapeutic Contexts

This study explores the use of Large language models to analyze therapist remarks in a psychotherapeutic setting. The paper focuses on the application of BERTopic, a machine learning-based topic modeling tool, to the dialogue of two different groups of therapists (classical and modern), which makes it possible to identify and describe a set of topics that consistently emerge across these groups. The paper describes in detail the chosen algorithm for BERTopic, which included creating a vector space from a corpus of therapist remarks, reducing its dimensionality, clustering the space, and creating and optimizing topic representation. Along with the automatic topical modeling by the BERTopic, the research involved an expert assessment of the findings and manual topic structure optimization. The topic modeling results highlighted the most common and stable topics in therapists speech, offering insights into how language patterns in therapy develop and remain stable across different therapeutic styles. This work contributes to the growing field of machine learning in psychotherapy by demonstrating the potential of automated methods to improve both the practice and training of therapists. The study highlights the value of topic modeling as a tool for gaining a deeper understanding of therapeutic dialogue and offers new opportunities for improving therapeutic effectiveness and clinical supervision.