Intelligent sentiment analysis with Arabic patient feedback on healthcare services in King Hussein Cancer Center.

The growing digitization of healthcare services has led to an abundance of textual patient feedback, offering a unique opportunity to assess healthcare quality through sentiment analysis. While most existing research focuses on English-language data, non-English contexts - especially Arabic dialects - remain underexplored. This study introduces JADKHCC, a novel corpus specifically designed for sentiment analysis of patient feedback written in the Jordanian Arabic dialect, collected from King Hussein Cancer Center (KHCC). The corpus is manually annotated using a comprehensive methodology to capture sentiments on both three- and five-point Likert scales. This study analyzes 15,812 Jordanian Arabic Dialect comments by employing various pre-processing techniques and feature vectors, including BERT-base-Arabic, Word2Vec, and FastText. Furthermore, the study considers a wide range of deep learning classifiers alongside balancing techniques to address data imbalance. The results demonstrate the superior performance of the CNN with BERT representation model compared to the BiLSTM, LSTM, RNN, and RNNLSTM models. The findings indicate an F1-score of approximately 96%, suggesting the potential for predicting patient sentiment from textual feedback. By automating feedback analysis, this approach enables KHCC to detect dissatisfaction, identify unmet needs, and act on key concerns promptly. It reduces the burden of manual review and supports data-driven service improvements-advancing KHCC's mission to deliver responsive, high-quality, patient-centered cancer care.