Harnessing Deep Neural Networks for Micro-Blog Sentiment Analysis: A Novel Approach

Analyzing sentiment in micro-blogs, such as tweets or short social media posts, is a challenging yet important task in natural language processing (NLP). Deep learning techniques have shown great promise in this domain. Here's a novel approach for sentimental analysis using a deep neural network for micro-blogs. With the exponential growth of user-generated content on online platforms, YouTube has emerged as a dominant space for both creators and viewers to interact. One crucial aspect of this interaction is the comment section, where viewers express their thoughts and sentiments. This research presents a robust sentiment analysis model tailored specifically for YouTube video comments, aiming to extract valuable insights from this rich source of user-generated content. Ultimately, our novel deep neural network approach for micro-blog sentiment analysis demonstrates promising results, offering valuable insights into public sentiment across various social media platforms Using a deep neural network, this study presents a sentiment analysis model for YouTube video comments. This research contributes to the advancement of sentiment analysis methodologies, addressing the unique challenges posed by the concise and informal nature of micro-blogs. Our method, a mathematical model, and individuals were utilized to categorize the comments on various blogs. It was assumed that the classification of humans was entirely accurate. The results showed that, with classification accuracy ranging from 90% to 97%, our model is more precise than the numerical model.