Subjective Well-being(SWB), which refers to how people ex-
perience the quality of their lives, is of great use to public policy-makers
as well as economic, sociological research, etc. Traditionally, the mea-
surement of SWB relies on time-consuming and costly self-report ques-
tionnaires. Nowadays, people are motivated to share their experiences
and feelings on social media, so we propose to sense SWB from the vast
user generated data on social media. By utilizing 1785 users' social media
data with SWB labels, we train machine learning models that are able
to \sense" individual SWB from users' social media. Our model, which
attains the state-by-art prediction accuracy, can then be used to identify
SWB of large population of social media users in time with very low cost.