Hot Topic Detection on Newspaper

Abstract

Online newspaper nowadays is gradually replacing the traditional one and the variety of articles on newspaper motivated the need for capturing hot topics to give Internet users a shortcut to the hot news. A hot topic always reflects the people's concern in real life and has big impact not only on community but also in business. In this paper, we proposed a novel topic detection approach by applying Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on Vector Space Model (VSM) to solve the challenge in noisy data and Pearson product-moment correlation coefficient (PMCC) on high ranking keywords to identify topics behind keywords. The proposed approach is evaluated over a dataset of ten thousand of articles and the experimental results are competitive in term of precision with other state-of-the-art methods.