Visualizing semantic space of online discourse: the knowledge forum case

Authors:
Bodong Chen
Affiliations:
University of Toronto, Toronto, Canada
Venue:
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Year:
2014

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Abstract

This poster presents an early experimentation of applying topic modeling and visualization techniques to analyze online discourse. In particular, Latent Dirichlet Allocation was used to convert discourse into a high-dimensional semantic space. To explore meaningful visualizations of the space, Locally Linear Embedding was performed reducing it to two-dimensional. Further, Time Series Analysis was applied to track evolution of topics in the space. This work will lead to new analytic tools for collaborative learning.