Naive Bayes Classifier with SMOTE for Sentiment Analysis of Blibli App Reviews on The Google Play Store - Kurniawan, Yogiek Indra; Sidiq, Rafli Hudanul; Widadi, Azzam Dicky Umar; Yubiharto, Afiftha Ravi Aufa; Gibran, Akhmad Khahlil; Aditama, Maulana Rizki; Laksono, FX Anjar Tri (Jurnal Penelitian Inovatif, 2025)
In the digital age, online shopping has become prevalent, with platforms like the Google Play Store enabling users to download and review mobile applications. This study aims to analyze the sentiment of user reviews for the Blibli application on the Google Play Store using the Naive Bayes Classifier, a simple yet effective algorithm for text classification tasks. A total of 2500 recent reviews were scraped using Google Colaboratory and the Python programming language. Data preprocessing steps included cleaning, stopword removal, tokenization, and stemming, followed by addressing class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE). The dataset was divided into training and testing sets in an 80:20 ratio. The Naive Bayes algorithm with SMOTE was employed for sentiment classification, yielding an accuracy of 90%, precision of 90%, recall of 92%, and an F1-score of 91%. These results demonstrate the model’s reliability in distinguishing between positive and negative sentiments, with a slight bias towards positive sentiments. Additionally, word cloud visualizations were generated to highlight frequently occurring words in both positive and negative reviews. The findings provide valuable insights for Blibli application developers and stakeholders, aiding in the assessment of user satisfaction and identification of areas for improvement. This research underscores the efficacy of the Naive Bayes Classifier in sentiment analysis and the utility of Google Colaboratory for data processing tasks.