🤖 AI Summary
This study addresses the lack of psycholinguistic text analysis tools tailored to the grammatical and cultural specifics of Russian. To bridge this gap, the authors present RusLICA, the first automated platform for Russian psycholinguistic analysis, which avoids direct translation of existing dictionaries and instead constructs an original psycholinguistic taxonomy grounded in native Russian corpora and semantic resources. Integrating lemmatization, multi-source semantic lexicons, corpus-based methods, and pretrained language models, RusLICA extracts 96-dimensional textual features and successfully maps lexical items to 42 psycholinguistic categories. The platform enables fine-grained quantification of linguistic and psychological characteristics in Russian texts and is publicly available online, thereby filling a critical void in computational psycholinguistics for the Russian language.
📝 Abstract
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.