With the advent of the Internet, more and more people prefer to post their feedback, reviews, and opinions through the social media platform, such as facebook, twitter, Chinese Weibo, and so on. Sentiment analysis of such data is becoming a fast and effective way of evaluating public opinion and sentiment for addressing practical problems, such as predicting consumer preferences or sensing public emotions    towards some products, services or issues. In addition, the current robots developed still cannot truly understand human languages. Therefore, the motivation of this research is two folds: (i) To Realize Human Like Real-World Deep Language Understanding of Text; and (ii) To Develop Human Like  Brand New AI Technologies for Big Data Analysis. As the first step, the presenter will introduce sentiment analysis of text comments, which includes learning based, non-learning based and hybrid methods. For learning based methods, different enhancements, such as feature selection, negation dealing, and emoticon handling are studied to improve the performance of existing machine-learning methods; for non-learning based methods or hybrid methods, a series of patentable technologies are proposed to address the limitations of the existing methods.