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Abstract

Background: Patient Online Reviews (PORs) provide real-time, unsolicited feedback that complements traditional satisfaction surveys. However, existing sentiment analysis tools often handle only monolingual data, broad sentiment categories, or lack review authenticity checks. This study develops a hybrid Aspect-Based Sentiment Analysis (ABSA) model combining RoBERTa with a rule-based aspect detection layer to extract fine-grained sentiment insights from Indian hospital reviews.

Methods: We curated and manually annotated 4,862 Google Maps reviews from 25 Indian hospitals across six aspects—Doctor Behavior, Staff Behavior, Cleanliness, Facility Experience, Cost, and Waiting Time. Though the scheme included positive/negative/neutral labels, the final model used binary polarity (positive vs. negative) for reliability given sparse neutral data. The hybrid pipeline integrated RoBERTa fine-tuning with a keyword dictionary to enhance recall on low-frequency or implicit mentions. Review authenticity was verified using Type-Token Ratio, Jaccard similarity, and bigram overlap. External validation used 670 independent reviews.

Results: The model achieved an average F1 score of 0.923 and 92.67% accuracy. Sensitivity and specificity averaged 0.905, with PPV 94.27% and NPV 87.82%. Comparative testing showed superior performance over lexicon-based and monolingual transformer models. A case study revealed strong satisfaction with clinical care but concerns over waiting time.

Conclusions: The hybrid ABSA framework converts unstructured patient feedback into structured, actionable insights for hospital dashboards, audits, and digital health systems, supporting quality improvement in resource-limited, multilingual settings.

Keywords: Sentiment analysis, Healthcare, Patient experience, Hybrid model, India

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