Vader Meets Multilingual Voices: Klasifikasi Sentimen Ulasan Pada Aplikasi Babble Dengan Bantuan Deep Translator - Yustida Bellini; Ayu Okta Pratiwi (PPPM Institut Teknologi Pagar Alam, 2025)
The rapid advancement of technology today greatly facilitates our access to information—within seconds, we can obtain whatever information we need. This also makes it easier to learn various languages around the world, as seen in the Babbel application. This study aims to identify sentiment in user reviews of the Babbel app by utilizing a combination of Deep Translator, VADER (Valence Aware Dictionary and Sentiment Reasoner), and Logistic Regression. User reviews were collected from the Google Play Store, resulting in 1,000 multilingual reviews. All reviews in different languages were translated into English using Deep Translator. After translation, sentiment labeling was performed using VADER. Then, the text data were transformed into numerical form using TF-IDF vectorization. After all these steps, the classification process was carried out using a Machine Learning model, namely Logistic Regression. The evaluation phase used a Confusion Matrix, and the sentiment classification achieved an accuracy of 89%. This study concludes that the combination of lexical-based analysis and machine learning can provide reliable results for multilingual sentiment analysis. In the future, this approach can be further developed by evaluating the performance of other classification algorithms.