All Publications

We investigate the benefits of PTMs for app review classification compared to the existing models, as well as the transferability of PTMs in multiple settings.

In this paper, we propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in short texts adaptively. AOBTM alleviates the sparsity problem in short-texts and considers the statistical-data for an optimal number of previous time-slices.

The developed web-browser based interactive visualization tool is a novel framework that makes it easier for the developers to traverse through the extensive result set generated by the text classification and topic modeling algorithms. It also helps developers to quickly comprehend the outcomes of implemented model feature combinations and algorithms used.

The information about Canadian Federal and Provincial Elections exists in numerous websites, as separate tables so that the user needs to traverse through a tree-like structure of scattered information on the site, and the user, at the end, is left with the comparison, on their own, without providing proper data interpretation tools. In this paper, we provide technical details of addressing the problem, by using the Canadian Elections data (since 1867).