Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
License:
init
Browse files
README.md
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@@ -26,7 +26,6 @@ pretty_name: TweetTopicSingle
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### Dataset Summary
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Topic classification dataset on Twitter with multiple labels per tweet.
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- Label Types: `arts_&_culture`, `business_&_entrepreneurs`, `celebrity_&_pop_culture`, `diaries_&_daily_life`, `family`, `fashion_&_style`, `film_tv_&_video`, `fitness_&_health`, `food_&_dining`, `gaming`, `learning_&_educational`, `music`, `news_&_social_concern`, `other_hobbies`, `relationships`, `science_&_technology`, `sports`, `travel_&_adventure`, `youth_&_student_life`
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## Dataset Structure
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{
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"date": "2021-03-07",
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"text": "The latest The Movie theater Daily! {{URL}} Thanks to {{USERNAME}} {{USERNAME}} {{USERNAME}} #lunchtimeread #amc1000",
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"id": 1368464923370676231,
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"label": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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"label_name": ["film_tv_&_video"]
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}
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### Data Splits
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### Citation Information
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### Dataset Summary
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Topic classification dataset on Twitter with multiple labels per tweet.
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## Dataset Structure
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{
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"date": "2021-03-07",
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"text": "The latest The Movie theater Daily! {{URL}} Thanks to {{USERNAME}} {{USERNAME}} {{USERNAME}} #lunchtimeread #amc1000",
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"id": "1368464923370676231",
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"label": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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"label_name": ["film_tv_&_video"]
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}
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### Data Splits
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| split | number of texts |
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| test | 1679 |
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| train | 1505 |
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| validation | 188 |
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| temporal_2020_test | 573 |
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| temporal_2021_test | 1679 |
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| temporal_2020_train | 4585 |
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| temporal_2021_train | 1505 |
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| temporal_2020_validation | 573 |
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| temporal_2021_validation | 188 |
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| random_train | 4564 |
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| random_validation | 573 |
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| coling2022_random_test | 5536 |
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| coling2022_random_train | 5731 |
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| coling2022_temporal_test | 5536 |
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| coling2022_temporal_train | 5731 |
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### Citation Information
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