Image sentiment analysis using latent correlations among visual, textual, and sentiment views

As Internet users increasingly post images to express their daily sentiment and emotions, the analysis of sentiments in user-generated images is of increasing importance for developing several applications. Most conventional methods of image sentiment analysis focus on the design of visual features, and the use of text associated to the images has not been sufficiently investigated. This paper proposes a novel approach that exploits latent correlations among multiple views: visual and textual views, and a sentiment view constructed using SentiWordNet. In the proposed method, we find a latent embedding space in which correlations among the three views are maximized. The projected features in the latent space are used to train a sentiment classifier, which considers the complementary information from different views. Results of experiments conducted on Flickr and Instagram images show that our approach achieves better sentiment classification accuracy than methods that use a single modality only and the state-of-the art method that jointly uses multiple modalities.