Abstract
Abstract: YouTube, a diverse content hub, acts as a prominent platform for both viewing videos and engaging through comments that express opinions. By carefully analyzing these comments, sentiments associated with products or topics become evident. This research delves into sentiment analysis of YouTube comments, specifically focusing on sentiments towards productivity tools and technologies. Through this examination of conveyed sentiments, it deepens understanding of public perceptions and attitudes toward these tools. Utilizing YouTube's reach and engagement, the study systematically collects comment data using the YouTube API and employs the established VADER sentiment analysis framework. Employing a Bag-of-Words approach, the research incorporates machine learning algorithms like Naive Bayes and Random Forest, achieving accuracy rates of 84.01% and 91.34%, respectively. In-depth temporal analysis of user engagement patterns uncovers trends, with heightened engagement followed by decline, correlating with external events, notably the Covid-19 pandemic. These insights enhance sentiment analysis and illuminate dynamics between societal occurrences, user sentiments, and digital dialogues, offering perspectives on evolving opinions about productivity tools and technologies
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