Visual Sentiment Analysis for Social Images Using Transfer Learning Approach
Jyoti Islam, Yanqing Zhang · 2016
Visual sentiment analysis framework can predict the sentiment of an image by analyzing the image contents. Nowadays, people are uploading millions of images in social networks such as Twitter, Facebook, Google Plus, and Flickr. These images play a crucial part in expressing emotions of users in online social networks. As a result, image sentiment analysis has become important in the area of online multimedia big data research. Several research works are focusing on analyzing the sentiment of the textual contents. However, little investigation has been done to develop models that can predict sentiment of visual content. In this paper, we propose a novel visual sentiment analysis framework using transfer learning approach to predict sentiment. We use hyper-parameters learned from a very deep convolutional neural network to initialize our network model to prevent overfitting. We conduct extensive experiments on a Twitter image dataset and prove that our model achieves better performance than the current state-of-the-art.