VippSent Dataset . In the field of Visual Sentiment Analysis (VSA), building image datasets labeled according to the emotions they evoke is essential, yet complex and prone to cognitive biases. Our dataset was developed through a scalable and customizable methodology, grounded in principles of semiotics and art theory, and is divided into three emotional classes: positive, negative, and neutral. The method combines common subjects (such as people, animals, and objects) with emotionally opposed adjective pairs and neutral adjectives identified through the semiotic square. It also includes artistic representations of emotional concepts, leveraging the ability of artists to express emotions and enhancing the dataset’s generalization capacity. This approach draws on the structures of visual language and the expressive power of art to support the classification of images based on the emotions they evoke, with a high degree of generalization. It is suitable for applications in areas such as marketing, opinion analysis, journalism, and the study of visual communication.