EMOTIONAL COMPUTING IN ABSTRACT ART ANALYSIS - Bhosale, Varsha Kiran; Homavazir, Malcolm; Singh, Hitesh; Goel, Pooja; Grover, Madhur; Bhatnagar, Tarang (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Combining emotional computers and abstract art results in another perspective to the way people feel upon looking at things that are not pictures. This paper uses machine learning and deep learning to investigate computer methods of determining how abstract art affects individuals. It applies computational aesthetics, and concepts of feeling in the visual perception towards developing a model of the impact of colours, textures, and shapes to the feelings of people. There are several emotion recognition mechanisms namely the RBF-SVM, the random forest, the resnet-50 and the vision transformer, which are tested on a rigorously selected set of abstract artwork to determine how they fare in classifying emotions. Image processing and deep learning techniques are employed to extract features and visual-semantic map which detects emotion indicators in artistic pieces. The method establishes the place of mixed inputs, written, visual, and environmental data to enhance emotional predictions. Data of physiological and psychological feelings are checked to explain whether computer conclusions are similar to the data that people observe. The proposed system design provides an avenue to mood analysis, which includes preparation, all the way up to model evaluation. This will be supported by measures such as accuracy, memory, F1-score, and association with human-answered answers. It tries to relate cognitive psychology and computer modelling by observing the ways in which machines may simulate emotional knowledge in abstract art by analyzing the disparities between algorithmic predictions and human subjectsive judgments.