@inproceedings{902c01b95d1f4646acbb795d9ea633d4,
title = "Visualizing semantic space of online discourse: The Knowledge Forum case",
abstract = "This poster presents an early experimentation of applying topic modeling and visualization techniques to analyze on- line 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.",
keywords = "Collaborative learning, Discourse analysis, Knowledge building, LDA, Semantic analysis, Text mining",
author = "Bodong Chen",
year = "2014",
doi = "10.1145/2567574.2567595",
language = "English (US)",
isbn = "1595930361",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "271--272",
booktitle = "LAK 2014",
note = "4th International Conference on Learning Analytics and Knowledge, LAK 2014 ; Conference date: 24-03-2014 Through 28-03-2014",
}