Word Embedding based News Classification by using CNN
Faisal Ahmed, Nazma Akther, Mohammad Shahidul Hasan, Kibtia Chowdhury, Md. Saddam Hossain Mukta · 2021
In this era of information technology, the number of online news portal is increasing day by day. These online news portals make a good profit by advertising different consumer products to their reader. However, due to the lack of intelligence, traditional news portals cannot identify what types of news are preferred by the users. As a consequence, these news portals most of the time show irrelevant advertisements to the readers and incur a great economic loss to the advertisers. If these news portals can identify what type of news a user is reading, then they can provide contextual advertisements (showing advertisements of news-related products) and gain more profit. Therefore, in this paper, we proposed a method integrating word embedding with Convolutional Neural Network (CNN) for the classification of English news into four different categories: Sports, Business, National and International. The performance of the proposed method is evaluated on our prepared dataset in terms of macro- f1 and micro-f1 scores. The experimental result shows that our proposed method achieved macro-f1 and micro-f1 scores of 0.90 and 0.89, respectively which are significantly higher than that of all the baseline methods.