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Abstract In view of the deficiency of domestic Weibo user attribute analysis, the imperfection of Weibo feature extraction and the problem that the classification accuracy needs to be improved, a Weibo user attribute analysis method based on multi-features is proposed. This paper first uses the word2vec model to build text features from Weibo content, then constructs Weibo user features from Weibo information and user information, and finally sends multi-feature sets into the improved three-tier stacking model to build Weibo user attribute analysis model. The experimental results show that this method has better classification effect than the text-based classification method and the traditional stacking model.
Cheng et al. (Fri,) studied this question.