Automatic Organization of Semantically Related Tags Using Topic Modelling.

The use of social media platforms such as social networks, weblogs and community question answering websites increased largely in the last few years. This increase in usage contributed to the vast explosion in available online content. Some of these platforms use social tagging, tags manually inserted by content authors, as a way to facilitate content description and discovery. In this paper, our goal is to automatically group semantically related tags in order to organize the large amount of tags contributed by various users. Our approach is based on using a topic model to discover topics of documents, and then grouping top tags related to documents assigned to each topic. We perform a set of experiments using different number of topics to provide different levels of details for the generated tag groups. The dataset used in our experiments is extracted from Stack Overflow, a community question answering website used by programming professionals. Tag groups generated by our technique are presented and evaluated.