Sentiment Analysis of User Comments for a YouTube Educational Videos

Jadhav, Sayali Sunil (2024) Sentiment Analysis of User Comments for a YouTube Educational Videos. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (781kB) | Preview
Preview	PDF (Configuration Manual) Download (836kB) | Preview

Abstract

Over the years, online learning has outgrown and has become a critical component for educational area. Variety of instructors and video makers based on various online platforms like Udemy, Coursera and YouTube have transformed education by providing anytime on-click courses, which attracts a global audience. While there is extensive research already been conducted on analysis of sentiments over traditional classrooms and other online learning content, but we see that less focus has been given to the vast repository of YouTube's user comment. This gap emphasizes the need to understand the emotional components of the educational information offered through this medium. This study bridges the gap between content providers and viewers by applying sentiment analysis to analyse the YouTube user comments on educational content providing a thorough examination of YouTube comments. Approaches like TF-IDF and NRC lexicon are used in combination to identify the sentiment polarity from negative to positive (-1 to 1). Visualizations in form of polarity graphs, helps understand sentiment distributions and their implications. Our approach gives instructors meaningful insights by allowing them to understand the audience responses, identify the growth areas and increase engagement of learners. It provides the instructors with useful insights on learner emotions and encourages them to improve the online learning experience. Future research intends to improve the emotion analysis and address the current issues in individualized education.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Milosavljevic, Vladimir UNSPECIFIED
Uncontrolled Keywords:	Sentiment Analysis; YouTube; Comments; Educational; Emotions; Learning; TF-IDF; Data pre-processing; NRC Lexicon; Polarity; Instructors
Subjects:	L Education > L Education (General) Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	02 Sep 2025 12:56
Last Modified:	02 Sep 2025 12:56
URI:	https://norma.ncirl.ie/id/eprint/8712

Actions (login required)

View Item