Weakly-Supervised Occupation Detection for Micro-blogging Users

In this paper, we propose a weakly-supervised occupation detection approach which can automatically detect occupation information for micro-blogging users. The weakly-supervised approach makes use of two types of user information (tweets and personal descriptions) through a rule-based user occupation detection and a MCS-based (MCS: a multiple classifier system) user occupation detection. First, the rule-based occupation detection uses the personal descriptions of some users to create pseudo-training data. Second, based on the pseudo-training data, the MCS-based occupation detection uses tweets to do further occupation detection. However, the pseudo-training data is severely skewed and noisy, which brings a big challenge to the MCS-based occupation detection. Therefore, we propose a class-based random sampling method and a cascaded ensemble learning method to overcome these data problems. The experiments show that the weakly-supervised occupation detection achieves a good performance. In addition, although our study is made on Chinese, the approach indeed is language-independent.