Integration of Machine Learning Methods into the Sociological Analysis of Client Experience in the Legal Services Market

<jats:p>The article explores the possibilities and methodological foundations for integrating machine learning methods into the sociological analysis of client experience in the legal services market. Using the LegalBench learned_hands_consumer corpus (620 complaint texts), the authors conducted an empirical comparison of an interpretable model (TF IDF + logistic regression) and a neural network model (fine tuned Jina-embeddings with LoRA). Based on error analysis and the confidence distribution, four behavioral types of clients are identified. A four level analytical system is proposed, and practical recommendations are formulated for optimizing client experience, including a two stage ML filter, query segmentation, and a human in the loop retraining cycle. The results demonstrate that machine learning can serve not only as a technical classifier but also as a tool for sociological reconstruction of digital traces of legal mobilization.</jats:p>