A study on trading using social big data analysis was conducted under the supposition that social members' collective emotion may affect on stock price. Social data is increasing, and is called as big data, it enables us to study social members' emotional flow which changes in real time. This study, we extracted emotions from social data with national language techniques, and found stocks highly related to emotional flow by statistic analaysis. Portfolio predicted stock price by monthly with machine learning and mean reversion strategy, and Black-Litterman's model was used as asset allocatin model. On this model, we conducted an experiment from Jananuary, 2011 to October, 2014, and the results exceeded 20% of KOSPI earning rate. We conducted another study from January, 2015 to August, 2015 on actual investment, and the result showed better performance compared to KOSPI earning rate by 12%. Through this research, we could verify the possibility of outperform compare with market performance of robotrading using AI based bigdata analysis.