Розробка моделей кластеризації холдерів розширених думок на основі агрегованих стилометричних та сентимен-ознак повідомлень у чатах

The subject of research is the methods and technologies for monitoring holder opinion groups in social media based on stylometric and sentiment features. One of the most important problems is the increasing complexity of text content, which makes user behavior analysis more difficult because of anonymity, informal language, slang, emojis, and non-standard writing styles. Stable, long-term behavioral patterns are not captured by methods based on single-message evaluation. This research proposes a holder-level clustering method based on aggregated stylometric and sentiment features taken from several messages per user. The methodology includes agglomerative hierarchical clustering, which is enhanced by decision tree analysis for feature selection and cluster interpretability, quantile normalization, dimensionality reduction via PCA (LiveJournal provided six components explaining 81.7% of the variance, while Instagram provided four components explaining 83.5% of the variance), and data preprocessing (VarianceThreshold, removal of highly correlated features). Ultimately, the majority of users were covered by two clusters for LiveJournal and three clusters for Instagram. The result is a set of clustering models that efficiently group holders into logical, understandable clusters based on their overall communication style and emotional expression. The primary advantages of the proposed approach are as follows: holder-level aggregation ensures stability and consistency in profiling; two-stage clustering with intermediate feature selection enhances explainability; the method demonstrates cross-platform applicability, validated on both LiveJournal and Instagram. As a result, over time, more accurate and dynamic user profiles can be developed, enabling improved sentiment analysis, automated moderation, and customized user interaction. This approach offers significant benefits over conventional single-message analysis methods in terms of results transparency, behavioral insight depth, and profile stability. Customized social media recommendations, automated moderation, and social sentiment analysis can all benefit from the study's findings.