Automatic image emotion classification is challenging because it requires models capable of recognizing emotion content in images, which can vary substantially. In addition, there was no image dataset with high quality labels large enough for learning these models until 2016. We have designed the system for Emotion Data Management and Analysis (SEDMA) not only for prediction of image emotion but also to actively improve the process of building high quality manually labeled datasets. SEDMA can potentially be used in a wide range of applications from automatic emotion recognition in smart devices to social media marketing decision-making. By using only 500 cinema-related images to fine tune a pre-trained deep learning model (Residual Network), a 59.1% top-2 class accuracy out of 8 classes was achieved through collaboration with Legendary Applied Analytics.