  It's of great importance to measure happiness of social network users, but
the existing method based on questionnaires suffers from high costs and low
efficiency. This paper aims at identifying social network users' happiness
level based on their Web behavior. We recruited 548 participants to fill in the
Oxford Happiness Inventory (OHI) and divided them into two groups with high/low
OHI score. We downloaded each Weibo user's data by calling API, and extracted
103 linguistic and behavior features. 24 features are identified with
significant difference between high and low happiness groups. We trained a
Decision Tree on these 24 features to make the prediction of high/low happiness
group. The decision tree can be used to identify happiness level of any new
social network user based on linguistic and behavior features. The Decision
Tree can achieve 67.7% on precision. Although the capability of our Decision
Tree is not ideal, classifying happiness via linguistic and behavior features
on the Internet is proved to be feasible.
