Visual Contents such as images and video does not only contain objects, location and actions but also cues about affect, emotion and sentiment. Such information I very useful to understand visual content beyond semantic concept presence thus making it more explainable to the user. Images are the easiest medium through which people can express their emotions on social networking sites. Social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Significant progress has been made with this technology, however, there is little research focus on the picture sentiments. This paper proposes a novel approach that exploits latent correlations among multiple views: visual and textual views, and a sentiment view constructed using SentiWordNet.