Analysis of sentimental images using deep learning approach

Abstract

Deep learning also known as universal learning approach is a kind of machine learning used to carry out classification tasks straightforwardly from Medias like images, text or sound. The paper centers on execution examination of three pre-trained deep learning network with an end goal of classification of images which are related to sentimental analysis. The pre-defined convolutional neural networks (CNN) handled are AlexNet, ResNet50 and VGG16 with different Epoch. These networks are pre-trained on Twitter dataset. We focus on the structure of feelings deduced by our model and contrast it with what has been proposed in the psychology literature, and confirm our model on a bunch of pictures that have been utilized in psychology studies.  At long last, our work likewise gives a helpful instrument to the developing scholarly investigation of pictures consists of both photographs and memes on social networks. The network architectures are analyzed dependent on different means including, accuracy, precision, recall and F1-score. As per the experiment, out of three networks AlexNet gives better outcome as far as precision when compared to other networks.