NLP-Driven User Behavior Analysis using Transformer-Based Models

Abstract

The capability to accurately interpret and predict user interactions is paramount for optimizing modern digital services, ranging from cyber security defences to hyper-personalized recommendation engines. Although conventional analysis techniques focus on structured behavioural logs, they often miss the deep, latent intent and nuanced context embedded within human communication streams, such as search queries, chat transcripts, and customer feedback.
This paper introduces a novel framework for Behavioural Textual Analysis that capitalizes on the advanced semantic modelling offered by Transformer architectures, specifically utilizing the Bidirectional Encoder Representations from Transformers (BERT) foundation. We detail the methodology for custom fine-tuning of pre-trained BERT models to transform sequential streams of user-generated text into high-dimensional, semantic vectors. These robust representations are subsequently applied to critical downstream applications, including the identification of anomalous behaviours and precise user intent mapping. Empirical results demonstrate that this context-aware, deep-learning approach substantially improves predictive performance compared to classical linguistic feature engineering (e.g., TF-IDF), effectively translating complex textual patterns into actionable insights for enhancing system safety, performance, and user-centric design.

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