Tagging Chinese microblogger via sparse feature selection

Abstract

In new media era, users post messages to record their daily lives and express their opinions via social media platforms, such as microblog. Recently, it is an attractive topic to tag users from the users generation contents. Tags for a microblog user, as the description for his/her interests, concerns or occupational characteristics, are playing an important role in user indexing, personalized recommendation, and so on. Previous works apply keyword extraction methods to present the interests of users. However, it is hard for keyword extraction to give accurate results when the data is deficient and noisy. In this paper, we propose a novel method to tag the users. Firstly, we apply feature selection via sparse classifier to generate preliminary tags for users. Then we also apply feature selection method to extend the tags. Finally, we refine the tags with a reranking strategy. We conduct our experiments on the data of the most popular Chinese microblog (Sina Weibo). The experimental results show that our method improves the performance significantly over other methods.