MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification

Overview

This dataset contains 10,917 news articles with hierarchical news categories collected between January 1st 2019, and December 31st 2019 classified by using NewsCodes Media Topic taxonomy. We manually labelled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories.

This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news.

Reproducibility of results

The results presented in the research paper "MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification", technical validation can be reproduced using functions in github repository.

Licenses

The dataset is made available under a CC-BY 4.0 license (see `LICENSE_DATA.txt`).