Sentiment Classification of FatSecret Application Reviews with Machine Learning Models
Abstract
In the current digital era, mobile applications have become an indispensable part of daily life, leading to a surge in user reviews as invaluable repositories of opinions. Health and fitness applications, such as FatSecret, generate millions of reviews rich with insights. However, specific sentiment analysis on FatSecret reviews using a structured Machine Learning (ML) approach remains limited. This study presents a comprehensive approach for sentiment classification of FatSecret application reviews using ML models. We collected Indonesian-language reviews from the Google Play Store, performed extensive data pre-processing (case folding, tokenization, filtering, normalization), and extracted features using Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW). Subsequently, we trained and evaluated five distinct sentiment classification algorithms: Random Forest, Decision Tree, Logistic Regression, SVM, and XGBoost, utilizing the StratifiedKFold method for automatic splitting in training and validation. Evaluation metrics include accuracy, precision, recall, and F1-score. The results of this research are expected to provide deep insights into user perceptions of FatSecret, identify favored and criticized features, and offer a replicable methodological framework for sentiment analysis of other applications in the future.
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