Insights from the crowd: sentiment analysis of YouTube comments using machine learning algorithms
Priyanshi Vikram Mulwani, Manisha Sunil Bhende, Swati Sharma, Poonam Yadav, Mussaratjahan Korpali, Bhavana Santosh Pansare, Meenal Wagh · 2025
In this era of technology, social media is widely used in every domain. The process of fetching, evaluating, and interpreting data from social media platforms to develop insightful information is known as social media analytics. Monitoring, measuring, and comprehending social media activity, trends, and user behavior entails utilizing a variety of technologies and approaches. Numerous goals can be achieved with social media analytics such as market research, customer service, brand management, marketing, and public opinion analysis. Sentiment analysis on YouTube can provide valuable visions into audience perceptions, preferences, and engagement with video content. This paper focuses on extraction of YouTube comments using video_id, carrying out sentiment analysis on these comments by using VADER python library. Further using machine learning (ML)algorithms like logistic regression and Naive Bayes classifier, we have built and trained a model and compared the accuracy of these models.