From Comments to Insights: Sentiment Analysis of YouTube Videos

Authors- Sujal Lothe, Komal Gulhane, Srushti Tembhare, Sujal Bhimanwar, Ashish Sawant

Abstract-Sentiment analysis is a method used to extract and interpret user opinions and perspectives on products or services. YouTube, as one of the foremost video-sharing platforms, generates millions of views and a vast array of comments. These comments provide valuable insights that can be utilized to enhance both the ratings and the overall quality of the content uploaded. Through natural language processing (NLP) and machine learning (ML) models, these comments can be analyzed in a highly effective manner. Research on sentiment analysis has explored numerous classification techniques, from binary models (positive or negative) and ternary models (positive, negative, neutral) to multi-class systems (such as joy, sadness, fear, surprise, and anger). However, identifying the most effective and precise method remains an ongoing challenge. In addition, sentiment analysis is frequently used to evaluate the emotional nuances expressed in comments on YouTube videos. This paper delves into various approaches and methodologies employed for sentiment analysis within the realm of YouTube videos. It systematically categorizes these techniques, offering significant insights for researchers engaged in the fields of data mining and sentiment analysis.

DOI: /10.61463/ijset.vol.13.issue2.243