MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification
- Resource Type
- Working Paper
- Authors
- Petukhova, Alina; Fachada, Nuno
- Source
- Data, 8(5), 74, 2023
- Subject
- Computer Science - Computation and Language
Computer Science - Artificial Intelligence
Computer Science - Machine Learning
- Language
This article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled 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.
Comment: The peer-reviewed version of this paper is published in Data at https://doi.org/10.3390/data8050074. This version is typeset by the authors and differs only in pagination and typographical detail