Abstract
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.
Keywords
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Acknowledgments
The authors gratefully acknowledge the generous support from National High-tech R&D Program of China (2013AA01A606), NSFC (61070115), Institute of Psychology (113000C037), Strategic Priority Research Program (XDA06030800) and 100-Talent Project (Y2CX093006) from Chinese Academy of Sciences.
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Nie, D., Li, L., Zhu, T. (2013). Conscientiousness Measurement from Weibo’s Public Information. In: Zhou, ZH., Schwenker, F. (eds) Partially Supervised Learning. PSL 2013. Lecture Notes in Computer Science(), vol 8183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40705-5_6
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DOI: https://doi.org/10.1007/978-3-642-40705-5_6
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