Hot Topic Detection on Newspaper
Tuan‐Dung Cao, Tat-Huy Tran, Thanh-Thuy Luu · 2018
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.