Analisis Sentimen Ulasan Pengguna Aplikasi Disney+ Hotstar Menggunakan Naive Bayes Classifier - Siharta, Niken Febrinikmah; Bhaswara, Maulana Muzakki; Firmantara, Wahyu; Sari, Anggraini Puspita (UNIVERSITAS SERAMBI MEKKAH, 2024)
This research suggests an approach using Naive Bayes classification to analyze the sentiment from the user reviews of the Disney+ Hotstar application on the Google Play Store. Review data is processed using the Naive Bayes classification method. This aims to create a text representation then Naive Bayes will group it into positive, negative or neutral categories. The expression results show that this model is able to overcome problems in terms of sentiment analysis with good accuracy. Evaluation uses several standards, namely accuracy, precision, recall, and F1-Score.With accuracy results of 81,6 %, precision of 58 %, recall of 83%, and f1-score of 68%. The results of this research shows useful information for application developers in improving the quality of their products. Apart from that, they can also see public sentiment towards the Disney+ Hotstar application. The research results can also be adapted to analyze sentiment on other applications available on digital platforms and have a positive impact on technological progress.