Sentiment Analysis Of Halodoc Application User Satisfaction Using The Naïve Bayes Method, SVM and LSTM
Keywords:
Sentiment Analysis, Halodoc, Naive Bayes, User Satisfaction, Text MiningAbstract
The growth of digital technology in the health sector has encouraged the emergence of various health service applications, one of which is Halodoc. This study aims to analyze the sentiment of user satisfaction with the Halodoc application through reviews left on the Google Play Store. The method used in this research is Naïve Bayes, with stages including data collection, preprocessing (case folding, cleansing, tokenizing, stopword removal, stemming), weighting using TF-IDF, and sentiment classification. This research uses 350 review data as a dataset. The evaluation results showed that the Naïve Bayes-based sentiment classification model achieved 84% accuracy, with the majority of user sentiments being positive. The findings illustrate that the Halodoc application is generally well received by its users, but still needs improvement in some aspects of the service.
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