Modeling of Online Learners’ Sentiments About Multigranularity Knowledge

Abstract

To provide insight into online learners’ interests in various knowledge from course discussion texts, modeling learners’ sentiments and interests at different granularities are of great importance. In this article, the proposed framework combines a deep convolutional neural network and a hierarchical topic model to discover the hidden structure of online learners’ sentiments about knowledge topics. The approach is to capture multigranularity knowledge of topics of interest to learners with the hierarchical topic model and to identify information about learners’ different sentiments with the convolutional neural network. This approach not only models knowledge of hierarchical interest from general to specific but also identifies learners and their sentiment orientations to better correspond to the different granularities of knowledge. The experimental results and analysis of real-world datasets show that the proposed approach is effective and feasible.