Abstract

Social media platforms have become pivotal arenas for the public to express emotions, opinions, and sentiments. While traditional sentiment analysis methods predominantly focus on textual data, they often overlook the rich emotional context embedded in images shared alongside posts. This paper presents a novel framework that integrates Visual Sentiment Analysis (VSA) with Natural Language Processing (NLP) techniques to enhance the understanding of public sentiment in social media content. By leveraging deep learning-based feature extraction from images (using pre-trained CNN models) and combining them with transformer-based text analysis (such as BERT), the proposed multimodal sentiment analysis model captures nuanced emotional expressions more effectively than unimodal approaches. Experiments conducted on benchmark datasets, including Twitter and Instagram posts, demonstrate a significant improvement in sentiment classification accuracy and contextual interpretation. The study highlights the potential of integrated sentiment analysis systems in applications such as brand monitoring, political opinion tracking, and mental health detection.