Copy of Kaggle dataset, adding to Huggingface for ease of use.
Description from Kaggle:
This dataset contains around 200k news headlines from the year 2012 to 2018 obtained from HuffPost. The model trained on this dataset could be used to identify tags for untracked news articles or to identify the type of language used in different news articles.
Each news headline has a corresponding category. Categories and corresponding article counts are as follows:
POLITICS: 32739 WELLNESS: 17827 ENTERTAINMENT: 16058 TRAVEL: 9887 STYLE & BEAUTY: 9649 PARENTING: 8677 HEALTHY LIVING: 6694 QUEER VOICES: 6314 FOOD & DRINK: 6226 BUSINESS: 5937 COMEDY: 5175 SPORTS: 4884 BLACK VOICES: 4528 HOME & LIVING: 4195 PARENTS: 3955 THE WORLDPOST: 3664 WEDDINGS: 3651 WOMEN: 3490 IMPACT: 3459 DIVORCE: 3426 CRIME: 3405 MEDIA: 2815 WEIRD NEWS: 2670 GREEN: 2622 WORLDPOST: 2579 RELIGION: 2556 STYLE: 2254 SCIENCE: 2178 WORLD NEWS: 2177 TASTE: 2096 TECH: 2082 MONEY: 1707 ARTS: 1509 FIFTY: 1401 GOOD NEWS: 1398 ARTS & CULTURE: 1339 ENVIRONMENT: 1323 COLLEGE: 1144 LATINO VOICES: 1129 CULTURE & ARTS: 1030 EDUCATION: 1004
This dataset was collected from HuffPost.
Can you categorize news articles based on their headlines and short descriptions? Do news articles from different categories have different writing styles? A classifier trained on this dataset could be used on a free text to identify the type of language being used.