Sentiment Analysis of Health Insurance Application Reviews Using Naïve Bayes, Support Vector Machine, and SMOTE
DOI:
https://doi.org/10.61591/jslhu.26.1087Keywords:
Sentiment analysis;, Naïve Bayes;, Support Vector Machine;, SMOTE ;, Health Insurance ApplicationAbstract
This study explores factors shaping user experience in mobile health insurance application by analyzing user reviews through sentiment classification. Two models such as Naïve Bayes (NB) and Support Vector Machine (SVM) were examined, with NB yielding higher precision and F1-score. Data preparation proved influential: applying the Synthetic Minority Oversampling Technique (SMOTE) improved average accuracy by 7.51%, slang normalization added a modest 0.25%, while including neutral sentiment reduced accuracy by 9.67%. The selected configuration, NB combined with SMOTE and slang replacement, achieved 93.53% accuracy, 93.65% precision, 93.53% recall, and an F1-score of 93.47%. Examination of reviews further shows that discussions about security, ease of use, and timeliness are dominated by negative sentiment.
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