KLASIFIKASI OPINI PENONTON FILM PADA PLATFORM STREAMING VIDEO DENGAN ALGORITMA NAIVE BAYES

Alita Tiffana; Erlin  Elisa

doi:10.33884/comasiejournal.v13i2.10328

Authors

Alita Tiffana universitas putera batam
Erlin Elisa Universitas Putera Batam

DOI:

https://doi.org/10.33884/comasiejournal.v13i2.10328

Keywords:

Sentiment Analysis, Data Mining, Naïve Bayes, Video Streaming, Netflix Disney

Abstract

Video streaming platforms have become an essential component of contemporary society since they offer flexible access to entertainment. However, opinions about these programs are still divided, with some members of the public being more supportive than others. This study aims to categorize viewer attitudes toward video streaming services, specifically Netflix and Disney+, into three groups: good, negative, and neutral. The strategy used is the Naïve Bayes Algorithm, and web scraping techniques are used to collect user comment data from the Google Play Store. Preprocessing, data labeling, classification, and model evaluation using metrics like accuracy, precision, recall, and F1 score are all part of the analytical process.The results of the investigation showed that the Gaussian Naïve Bayes generated an accuracy of 43.52% for Disney+ and 41.99% for Netflix. This study shows that automated public opinion analysis is initially feasible, despite its current low degree of accuracy. Disney+ technically does better in classification, while Netflix gets more favorable reviews based on user assessments based on ratings. It is hoped that this research will provide the basis for more accurate opinion analysis instruments in the future.

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