This paper explores the use of machine learning and natural language processing (NLP) techniques for the automatic classification of text-based appeals in the field of housing and communal inspection (HCI). The proposed approach includes a data processing module and a set of classification models. An experimental study was conducted comparing traditional classification algorithms such as Naive Bayes, Support Vector Machines, Decision Trees, K-Nearest Neighbors (KNN), FastText, and transformer-based models using BERT. The approach—combining preprocessed data with sampling based on class distribution in the training set and leveraging top predictions from multiple models—demonstrated effective performance based on evaluation metrics including accuracy, top-k accuracy, F1-score, and ROC-AUC. This method significantly reduces the workload on human operators and improves service quality in the HCI sector.