M
Mohammad Abdul Hadi
Researcher at University of British Columbia
Publications - 8
Citations - 15
Mohammad Abdul Hadi is an academic researcher from University of British Columbia. The author has contributed to research in topics: Topic model & Visualization. The author has co-authored 8 publications.
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Proceedings ArticleDOI
AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
TL;DR: Adaptive Online Biterm Topic Model (AOBTM) is proposed to model topics in short texts adaptively to alleviate the sparsity problem in short-texts and considers the statistical-data for an optimal number of previous time-slices.
Proceedings ArticleDOI
ReviewViz: assisting developers perform empirical study on energy consumption related reviews for mobile applications
TL;DR: In this paper, a visualization tool is developed to empirically study machine learning algorithms and text features to automatically identify the energy consumption specific reviews with the highest accuracy, which makes it easier for the developers to traverse through the extensive result set generated by the text classification and topic modeling algorithms.
Posted Content
Geo-Spatial Data Visualization and Critical Metrics Predictions for Canadian Elections
TL;DR: The technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges are provided, and the developed tool contains data visualization, trend analysis, and prediction components.
Proceedings ArticleDOI
Geo-Spatial Data Visualization and Critical Metrics Predictions for Canadian Elections
TL;DR: In this paper, the authors provide technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges.
Posted Content
AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
TL;DR: In this article, the authors proposed Adaptive Online Biterm Topic Model (AOBTM) to model topics in short texts adaptively, which alleviates the sparsity problem in short-texts and considers the statistical data for an optimal number of previous time-slices.