This document discusses subjective information extraction from social media text. It begins by outlining a progression from coarse-grained to fine-grained analysis, and from static to dynamic models. It then describes approaches for extracting candidate sentiment expressions, identifying relations between expressions, and assessing target-dependent sentiment polarity. Finally, it provides examples and discusses applications like predicting election results based on analyzing sentiments expressed by different user groups on social media.