YouTube Comment Scraping and Sentiment Analysis Using Python and Machine Learning

Abstract

YouTube has become a massive platform for video sharing and discussion, with millions of users expressing their thoughts and opinions through comments. For content producers, marketers, and academics, gleaning significant insights from these comments can yield useful data. This project explores the combination of machine learning and web scraping techniques to scrape YouTube comments and perform sentiment analysis. By leveraging machine learning algorithms, we aim to analyze sentiments, opinions, and trends within the vast amount of textual data generated on the platform. The process involves collecting comments using the YouTube Data API or web scraping libraries, followed by preprocessing steps to clean and transform the data. Next, Machine learning methods like Naive Bayes, SVM, or deep learning models are used to train sentiment analysis models. These approaches include Bag-of-Words, TF-IDF, and word embedding. Our ability to comprehend the overall emotion conveyed by viewers is subsequently made possible by the trained models, which are then utilized to anticipate the sentiment of fresh comments. Additionally, the ability to identify very favorable or negative remarks is made possible by sorting comments based on sentiment scores. It is essential to consider the complexities of language and contextual nuances when conducting sentiment analysis. For many players in the YouTube ecosystem, this technique does provide useful insights into audience feedback, content strategy, and data-driven decision-making.