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It's of great importance to measure happiness of social network users, butthe existing method based on questionnaires suffers from high costs and lowefficiency. This paper aims at identifying social network users' happinesslevel based on their Web behavior. We recruited 548 participants to fill in theOxford Happiness Inventory (OHI) and divided them into two groups with high/lowOHI score. We downloaded each Weibo user's data by calling API, and extracted103 linguistic and behavior features. 24 features are identified withsignificant difference between high and low happiness groups. We trained aDecision Tree on these 24 features to make the prediction of high/low happinessgroup. The decision tree can be used to identify happiness level of any newsocial network user based on linguistic and behavior features. The DecisionTree can achieve 67.7% on precision. Although the capability of our DecisionTree is not ideal, classifying happiness via linguistic and behavior featureson the Internet is proved to be feasible.

Classify Sina Weibo users into High or Low happiness Groups Using Linguistic and Behavior Features