Multilingual Sentiment Analysis of RCTI+ Reviews Utilising Orange, ChatGPT, and Naïve Bayes - Juita, Meilani Mega; Rahmi, Rahmi (Journal of Information Technology and Computer Science, 2025)
Low user ratings on mobile applications often reflect underlying dissatisfaction that is not immediately apparent through quantitative scores alone. To uncover the sentiment dynamics behind such evaluations, this study analyzes user reviews of the RCTI+ Superapp in both the original Indonesian and English-translated forms. Using the CRISP-DM framework, reviews were scraped from Google Play, normalized and translated via ChatGPT, and classified using Orange Data Mining with a Naïve Bayes algorithm. The analysis reveals that English-translated reviews yield sharper sentiment polarity and higher classification accuracy (100%) compared to the original Indonesian texts (99.1%), albeit with reduced lexical nuance. These findings suggest that generative AI-assisted translation enhances sentiment clarity in informal, low-resource language data, while potentially simplifying cultural or emotional expression. The study offers methodological insights for multilingual sentiment analysis and practical implications for app developers seeking to interpret user feedback more effectively across languages.