Sentiment Analysis Of Online Learning Based On Tiktok Comments Using The Naive Bayes Algorithm

Aripun Aripun; Ujang Juhardi; Dandi Sunardi; Muhammad Husni Rifqo

doi:10.63922/ijeter.v4i03.6227

Aripun Aripun Universitas Muhammadiyah Bengkulu
Ujang Juhardi Universitas Muhammadiyah Bengkulu
Dandi Sunardi Universitas Muhammadiyah Bengkulu
Muhammad Husni Rifqo Universitas Muhammadiyah Bengkulu

DOI: https://doi.org/10.63922/ijeter.v4i03.6227

Keywords: Sentiment Analysis, TikTok, Online Learning, Naive Bayes, TF-IDF, Text Mining.

Abstract

ABSTRACT

Online learning is a form of information technology utilization in education that enables the learning process to be conducted without limitations of space and time. The development of online learning has generated various public responses, which can be found through user comments on social media platforms, particularly TikTok. This study aims to analyze public sentiment toward online learning based on TikTok comments using the Naive Bayes algorithm. The research data consisted of 1,193 comments collected through web scraping. The research stages included preprocessing, consisting of cleaning, case folding, word normalization, tokenization, stopword removal, and stemming. The data were then labeled using the Lexicon-Based method and feature weighting was performed using TF-IDF. Subsequently, the data were classified into three sentiment categories, namely positive, negative, and neutral, using the Naive Bayes algorithm. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the Naive Bayes algorithm achieved an accuracy of 60.37%. These findings indicate that the Naive Bayes algorithm can be used to identify public sentiment toward online learning based on TikTok comments.

Keywords: Sentiment Analysis, TikTok, Online Learning, Naive Bayes, TF-IDF, Text Mining.

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