Dynamic Fusion of Text, Video and Audio models for Sentiment Analysis

The paper aims to propose a technique for sentiment analysis that combines audio, textual, and visual data collected from customer input on social media sites such as Instagram, Facebook, and Twitter, as well as feedback forms and product evaluations. The feelings of individuals are represented in these online social media reviews. As a result, sentiment research will be essential for helping the growth of enterprises and individuals. The work assigns variable weights using recording technique for different modules and subsequently fusing it. This enables us to determine which model is optimal for sentiment analysis and how each model affects the final results. A system that can extract information about people's feelings regarding a certain product from text, audio, and video. We may utilize model predictions to determine the amount of consumer happiness with the product, enabling the company to take the required steps to enhance the product.