Analyzing the Changes in Online Community based on Topic Model and Self-Organizing Map

In this paper, we propose a new model for two purposes: (1) discovering communities of users on social networks via topics with the temporal factor and (2) analyzing the changes in interested topics and users in communities in each period of time. This model, we use Kohonen network (Self-Organizing Map) combining with the topic model. After discovering communities, results are shown on output layers of Kohonen. Based on the output layer of Kohonen, we focus on analyzing the changes in interested topics and users in online communities. Experimenting the proposed model with 194 online users and 20 topics. These topics are detected from a set of Vietnamese texts on social networks in the higher education field.

@article{Ho2015,
  title     = {Analyzing the Changes in Online Community based on Topic Model and Self-Organizing Map},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {7},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Thanh Ho and Phuc Do},
  doi       = {10.14569/IJACSA.2015.060715},
  url       = {https://doi.org/10.14569/IJACSA.2015.060715}
}