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All Journal International Journal of Engineering, Science and Information Technology
Senjaya, Andrian
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Sentiment Analysis of Vidio Application Based on Reviews on Google Play Store Using Bidirectional Encoder Representations from the Transformers Method Refianti, Rina; Senjaya, Andrian
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1124

Abstract

Sentiment analysis is a computational study that aims to process, extract, summarise, and analyse the information contained in the text so it can conclude the emotions and points of view given by the author from the text and share the emotional tendencies in the text through the subjective information contained in it. Vidio is a video streaming site that allows users to watch and enjoy various videos and other services, such as live chat and playing games over the internet, and broadcast them by live streaming and video on demand. The analysis process uses the Bidirectional Encoder Representations from Transformers (BERT) method to classify comments into positive, neutral, and negative sentiments using the Python programming language, and based on the results of the tests that have been carried out from the amount of comment data—as much as 6000 data with training data as much as 4019 data, validation data as many as 1154 data, and test data as many as 569 data—an accuracy result of 76%.
Co-Authors Rina Refianti