Optimized Support Vector Machine Model for Visual Sentiment Analysis

This research aims to develop an automatic visual sentiment analysis (VSA) model using an optimization-based support vector machine (SVM). Initially, the input images' features are extracted from the weighed-FC8 layer of the pretrained ResNet-18, where the relief algorithm evaluates the updated weight. On the other hand, the SVM classifier is tuned optimally using a hybrid optimization technique called Holoentropy Life Choice Optimization (HELMCO) algorithm. HELMCO has the characteristic features of both the Life Choice Based Optimization (LCBO) algorithm and the Cross entropy (CE) method. The analysis is done using the Emotion- 6 and Abstract Art_photo datasets based on performance parameters, such as Accuracy, Sensitivity, and Specificity. The accuracy of the proposed model is 70.7% using the Emotion-6 dataset and 76.8% using the Art_photo Dataset.