A Novel Approach to Categorize News Articles From Headlines and Short Text
Rafid Abyaad, Md Rayhan Kabir, Sakib Hasan · 2020 IEEE Region 10 Symposium (TENSYMP) · 2020
Over the last few years the world has experienced a surge in the number of online news portals. This has caused the volume of news articles to reach an all time high; which will only get higher with time. Thus, an efficient system of categorization and organization of the articles has become a necessity for various information systems like- news aggregation and association in search engines. It is impractical to employ humans to label this expansive volume of text data, prompting the growth of automated text categorization systems. And so, we devised a deep learning model that effectively categorizes news articles from the headlines and short text descriptions. The prime foci of our work were to design, develop, and measure the performance metrics of our proposed model.