Enhancing Multimodal Sentiment Analysis with Deep Learning Techniques to Foster Emotional Intelligence

Sentiment analysis (SA) is a critical process in understanding the emotions associated with data on social networking sites. Building upon this foundational concept, this paper introduces a sophisticated web application aimed at enabling users to submit diverse forms of content, including text, images, videos, and audio, for hate content assessment. This platform employs BERT CNN and VGG16 CNN algorithms for performing a thorough analysis of provided information in various formats. Emphasizing the identification of emotions in text and the detection of hate speech, the methodology extends to incorporate facial emotion detection for images. Through meticulous preprocessing, feature extraction, and ensemble learning, the proposed solution provides a robust framework for accurately identifying and mitigating hate content across various modalities. Experimental tests employing varied datasets validated the usefulness of the strategy, contributing to the advancement of hate content detection systems and fostering a safer online environment.