--- language: - en license: - other multilinguality: - monolingual size_categories: - 1k<10K task_categories: - text-classification task_ids: - sentiment-classification pretty_name: TweetTopicSingle --- # Dataset Card for "cardiff_nlp/tweet_topic_single" ## Dataset Description - **Paper:** TBA - **Dataset:** Tweet Topic Dataset - **Domain:** Twitter - **Number of Class:** 6 ### Dataset Summary Topic classification dataset on Twitter with single label per tweet. - Label Types: `arts_&_culture`, `business_&_entrepreneurs`, `pop_culture`, `daily_life`, `sports_&_gaming`, `science_&_technology` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ```python { "text": "Game day for {{USERNAME}} U18\u2019s against {{USERNAME}} U18\u2019s. Even though it\u2019s a \u2018home\u2019 game for the people that have settled in Mid Wales it\u2019s still a 4 hour round trip for us up to Colwyn Bay. Still enjoy it though!", "date": "2019-09-08", "label": 4, "id": 1170606779568463874, "label_name": "sports_&_gaming" } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co./datasets/tner/tweet_topic_single/raw/main/dataset/label.single.json). ```python { "arts_&_culture": 0, "business_&_entrepreneurs": 1, "pop_culture": 2, "daily_life": 3, "sports_&_gaming": 4, "science_&_technology": 5 } ``` ### Data Splits ### Citation Information ``` TBA ```