YouTube Comment Analyzer Using Sentimental Analysis

Abstract

This paper introduces a novel YouTube comment analyzer leveraging sentiment analysis techniques to provide insights into user engagement and opinion dynamics within the platform. With the exponential growth of YouTube as a primary source of online content consumption, understanding the sentiments expressed in user comments has become increasingly important for content creators, marketers, and platform moderators. Our proposed analyzer employs state-of-the-art natural language processing algorithms to categorize comments into positive, negative, or neutral sentiments, enabling a comprehensive examination of user feedback. Through the analysis of sentiment trends across diverse video categories and the identification of influential comment threads, our approach offers valuable insights into audience preferences, content reception, and community interactions. We present the methodology employed for data collection, preprocessing, sentiment analysis, and evaluation, utilizing a rich dataset of YouTube comments spanning various topics and demographics. The results showcase the effectiveness of our approach in uncovering underlying sentiments and identifying patterns of user engagement. This research contributes to the broader understanding of sentiment dynamics in online social platforms and provides practical implications for content creators to enhance audience satisfaction and optimize content strategies.