VISUAL SENTIMENT MINING IN CONTEMPORARY ART REVIEWS - R. Shobana; Vatharkar, Poonam Shripad; Riyat, Sonia; Singh, Dukhbhanjan; Kharadi, Meeta; Savant, Ila Shridhar (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Visual sentiment mining is a recent field of interdisciplinary study with potential to use computational methods to analyze the emotional reactions to visual art. Written by critics, curators and viewers, contemporary art reviews are full of rich affective meanings, which are not only visual stimuli but also cultural context. But the current sentiment analysis methods are mainly text based or geared towards general images and thus cannot capture the subtle emotional connotations inherent in works of art. The paper presents a single visual sentiment mining model that is specially developed towards modern art reviews, through the concurrent analysis of the images of works of art and the textual content of the reviews. Simultaneously, language models based on transformers are used to derive fine-grained sentiment and emotion signals in art reviews and detect the tone of the subjectivity and metaphorical language and the intent of a critique. A professional dataset is multimodal and is made as a combination of high-resolution images of artwork and professionally written reviews and annotations of emotion. These are rigorous preprocessing, cross-modal alignment, and optimized hyperparameter supervised learning as part of the experimental approach. The analysis is performed based on conventional measurements (accuracy and F1-score) as well as a suggested index of emotion coherence to understand the correspondence between the visual and textual moods.