Social media contents based sentiment analysis and prediction system

With the influence and social ripple effect of social media sites, diverse studies are in progress to analyze the contents generated by users. Numerous contents generated in real time contain information about social issues and events such as natural disasters. In particular, users show not only information about the events that occurred but also their sentiments. In this paper, we propose Polaris, a system for analyzing and predicting users' sentimental trajectories for events analyzed in real time out of the massive social media contents, and show the results of preliminary validation work that we have done. We show both trajectory analysis and sentiment analysis so that users can obtain the insight at a glance. Also, we increased the accuracy in sentiment analysis and prediction by making use of the latest deep-learning technique.