aspg-18-4174-1792025545 20261015005225 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 M. Sivasankar PG Scholar, Department of Computer Science and Engineering, Hindusthan Institute of Technology, India K. Murugan Assistant Professor, Department of Computer Science and Engineering, Hindusthan Institute of Technology, India P. Gouthami Assistant Professor, Department of Computer Science and Business Systems, Dr. N.G.P Institute of Technology, India G. Balambigai Assistant Professor, Department of EEE, Akshaya College of Engineering and Technology, Coimbatore, India Kalaivani T. 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mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} 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. 2025 389 397 4174 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170127 https://americaspg.com/journal/18/article/4174 https://americaspg.com/storage/51762448913.pdf