In this paper, we propose a method for identifying hidden principal sentiments among large scale texts from documents, social data, internet and blogs by analyzing standard language, slangs, argots, abbreviations and emoticons in those words. The IRLBA(Implicitly Restarted Lanczos Bidiagonalization Algorithm) is used for principal component analysis with large scale sparse matrix. The proposed system consists of data acquisition, message analysis, sentiment evaluation, sentiment analysis and integration and result visualization modules. The suggested approaches would help to improve the accuracy and expand the application scope of sentiment analysis in social data.