Implementation Naive Bayes Algorithm in Sentiment Analysis for  Netflix Reviews on Google Playstore

Aldy Mahendra; Anugerah Bagus Wijaya; Dani Arifudin

doi:10.47065/bits.v6i4.6646

Aldy Mahendra * Universitas Amikom Purwokerto, Purwokerto, Indonesia
Anugerah Bagus Wijaya Universitas Amikom Purwokerto, Purwokerto, Indonesia
Dani Arifudin Universitas Amikom Purwokerto, Purwokerto, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v6i4.6646

Keywords: Algorithm Naive Bayes; Data Mining; User Reviews; Netflix; Streaming Apps

Abstract

Developing technology has had an impact in many ways such as watching a movie, in the past watching a movie had to be through television or cinema media. This resulted in difficulties due to inefficient broadcast schedules, as well as the inability to replay. But unlike the current era, watching movies can be done anywhere and anytime by utilizing a streaming application. One of the existing applications is Netflix, in this application there are many types of movies from all walks of life, not only that the monthly subscription system in this application is relatively cheap. The existing streaming applications are not only Netflix but there are still many other applications, therefore Netflix needs to pay attention to the reviews given by users so that this application continues to grow and is not defeated by other applications. Reviews given by users can be used as evaluation material, to see reviews cannot be seen at a glance but must be detailed. Because there are quite a lot of reviews that manual processes cannot be applied, it is necessary to use a sentiment analysis process and utilize existing algorithms such as data mining. This study aims to conduct sentiment analysis based on user reviews of the Netflix application on the google playstore. The review data of 1,000 was taken by the author and then obtained the results of the research that the reviews given by users tended to be negative and the naïve bayes algorithm got an accuracy level of 82%.

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