Ensemble-Driven Multilingual Sentiment Analysis Framework for YouTube Comments with Dashboard

In the present-day digital age, sites like YouTube produce a vast volume of multilingual user comments. These comments are perfect for sentiment analysis because they contain a wealth of viewpoints and feelings. This article investigates how sentiments might be extracted from multilingual YouTube comments to improve user engagement and content comprehension. Our method divides sentiments into categories such as positive, negative, mixed feelings, and non-relevant using sophisticated natural language processing (NLP) techniques. This study illustrates the difficulties related to linguistic diversity and context sensitivity while also showcasing the efficacy of sentiment analysis in capturing the subtleties of user thoughts through the use of a large dataset of YouTube comments. The generated insights are meant to inform marketers and content creators about the preferences and opinions of their audiences in order to inform strategic decision-making. The development of an interactive dashboard will also facilitate the visualization of sentiment patterns in real-time, enabling stakeholders to engage with their audiences on a deeper level. This study demonstrates the potential of multilingual sentiment analysis as a tool for understanding global audience dynamics, developing the field, and enhancing the performance of automated content evaluation systems.