Shyness Trait Recognition for Schoolchildren via Multi-View Features of Online Writing

Shyness trait is a double-edged personality trait, and could pose a risk in early childhood for later adjustment difficulties. Therefore, it is necessary to pay attention to children with shyness trait and properly supervise them in early education, where the key problem is shyness trait recognition. Although some psychological methods of shyness measurement have been presented, they are high-cost and can only get a one-sided result limited by the targeted subjects. To develop an automated method of shyness trait recognition, we collect a dataset, containing online writing data of 1,754 schoolchildren from an educational website and ground truth labels obtained by a professional scale. The natural implicitness makes shyness trait difficult to be observed from a single point of view and the class imbalanced problem increases the challenge. In this article, a novel shyness trait recognition framework is proposed, which extracts multi-view features of online writing, including document-view, sentence-view and temporality-view ones. Different strategies with different features are applied to each single-view prediction and the multi-view prediction is made by a weighted voting ensemble. To verify the effectiveness, extensive experiments are conducted on the real-world dataset, demonstrating that the multi-view prediction significantly outperforms each single-view prediction and some advanced models of multi-view learning.