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

TL;DR

This paper applies machine learning techniques to analyze YouTube comments related to video games, revealing user sentiments and trends to aid game development and improve user experience.

Contribution

It introduces a sentiment analysis framework using machine learning on YouTube comments about video games, highlighting SVM's superior performance in this domain.

Findings

SVM achieved the highest classification accuracy

Sentiment analysis revealed user preferences and critiques

Advanced NLP techniques can enhance understanding of gaming community sentiments

Abstract

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\"ive 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.