Comparative Analysis of Naïve Bayes Algorithm Performance in English and Indonesian Text Sentiment Classification on Duolingo Application in Playstore - Andi Serlina; Abdul Rahim; Arbansyah (Teknika, 2025)
Text classification is an important topic in Natural Language Processing (NLP), especially when conducting research on user reviews on language learning apps such as Duolingo. This study compares the effectiveness of the Naïve Bayes algorithm in identifying sentiment in English and Indonesian reviews on the Duolingo app on Playstore. The approach includes data collection, text preparation (case folding, tokenization, stopword removal, and stemming), and Naïve Bayes algorithm evaluation for each dataset. Model performance was evaluated using accuracy, precision, recall, and F1-score. The Naïve Bayes method obtained 84% accuracy on the English dataset with a 90:10 data split and 67% accuracy on the Indonesian dataset with the same split ratio. The difference in the results obtained is due to several variables, including the use of informal language, slang, and more complicated word variants in Indonesian, which make proper classification more difficult for the model to achieve.