Analysis and Comparison of Dimensionality Reduction Techniques for News Blog Classification

News Blog Text-based Classification often relies on using deep learning and other complicated techniques, which are computationally expensive. In this paper, we try to analyze dimensionality techniques applied on Supervised Learning-based classifiers. This approach introduces supplementary-low-cost precursor techniques for getting a rough idea of news-reader trends and headline preferences of writers. These have potential applications in Search Engine Optimization for writers, News Advertising, Fake News detection, and many others.