社会与工程心理学研究室知识库

We apply a graph-based semi-supervised learning algorithm to identify the conscientiousness of Weibo users. Given a set of Weibo users’ public information(e.g., number of followers) and a few labeled Weibo users, the task is to predict conscientiousness assessment for numeric unlabeled Weibo users. Singular value decomposition(SVD) technique is taken for feature reduction, and K nearest neighbor(KNN) method is used to recover a sparse graph. The local and global consistency algorithm is followed to deal with our data. Experiments demonstrate the advantage of semi-supervised learning over standard supervised learning when limited labeled data are available.