Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content

The rapid evolution of the gaming industry, driven
by technological advancements and a burgeoning community,
necessitates a deeper understanding of user sentiments, especially
as expressed on popular social media platforms like YouTube.
This study presents a sentiment analysis on video games based
on YouTube comments, aiming to understand user sentiments
within the gaming community. Utilizing YouTube API, comments
related to various video games were collected and analyzed
using the TextBlob sentiment analysis tool. The pre-processed
data underwent classification using machine learning algorithms,
including Na¨ıve Bayes, Logistic Regression, and Support Vector
Machine (SVM). Among these, SVM demonstrated superior
performance, achieving the highest classification accuracy across
different datasets. The analysis spanned multiple popular gaming
videos, revealing trends and insights into user preferences and
critiques. The findings underscore the importance of advanced
sentiment analysis in capturing the nuanced emotions expressed
in user comments, providing valuable feedback for game developers to enhance game design and user experience. Future research
will focus on integrating more sophisticated natural language
processing techniques and exploring additional data sources to
further refine sentiment analysis in the gaming domain.