Analisis Sentimen Ulasan Pengguna Aplikasi Netflix Pada Google Play Menggunakan Algoritma Naïve Bayes
DOI:
https://doi.org/10.30865/klik.v4i6.1964Keywords:
Sentiment Analysis; Naïve Bayes; RapidMiner; Google Play StoreAbstract
The rapid development of information technology has advanced rapidly, including advancements in film technology. In this modern era, watching movies no longer requires going to the cinema, as there are applications available to watch movies anytime and anywhere. One popular application for watching movies is Netflix, a widely used streaming platform for films and series. Netflix also ranks 10th in terms of access in Indonesia. This study focuses on identifying user satisfaction levels with the Netflix application based on reviews on the Google Play Store. The research aims to analyze user review sentiment of an application available on Google Play, namely Netflix. These reviews will be used to gauge user satisfaction with the Netflix application. Researchers obtained these reviews using a Python web scraper with a total of 1000 unprocessed data points. After processing these 1000 data points by removing duplicates and symbols, researchers obtained 893 data points ready for sentiment analysis using RapidMiner. Out of the 893 data points, researchers manually labeled 635 data points, while 258 data points were labeled automatically using machine learning, namely Naive Bayes. Researchers also created a confusion matrix to determine the accuracy level of the algorithm used in this study. The accuracy result of the confusion matrix obtained by researchers in this study is 93.39%. The positive class precision value of 85.52% indicates that most positive reviews were identified accurately, while the negative class precision value of 100% demonstrates excellent capability in identifying negative reviews. In conclusion, the Netflix application receives diverse responses from users, and the algorithm used effectively identifies reviews accurately
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