Aspect-Based Sentiment Analysis on Healthcare Services Uding pre-trained Languges Model

Abstract

This research explores the application of various computational models for aspect- based sentiment analysis (ABSA) of healthcare reviews, a critical component of

enhancing healthcare services through feedback analysis. With the rapid expansion of online health platforms, the volume of textual reviews generated by patients provides a rich source of data for understanding patient satisfaction and areas needing improvement. The research thoroughly assesses various models, encompassing conventional statistical models, recurrent neural networks (RNNs), and sophisticated transformer-based models like BERT, RoBERTa, and DistilBERT. Each model was assessed based on its ability to accurately classify sentiments tied to specific aspects of healthcare services, such as cleanliness, staff behavior, and treatment efficacy. Two primary feature extraction techniques, Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), were employed to transform raw text into a suitable format for model ingestion. Our findings demonstrate that while traditional models offer quick and interpretable results, they sometimes lack the nuanced understanding of context provided by more sophisticated deep learning and transformer models. RNNs, particularly LSTM and BiLSTM, were effective in capturing temporal dependencies in text data, essential for comprehending longer patient feedback.