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---
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_multi"
## Dataset Description
- **Paper:** TBA
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6
### Dataset Summary
Topic classification dataset on Twitter with multiple labels per tweet.
- 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`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```python
{
"date": "2021-03-07",
"text": "The latest The Movie theater Daily! {{URL}} Thanks to {{USERNAME}} {{USERNAME}} {{USERNAME}} #lunchtimeread #amc1000",
"id": 1368464923370676231,
"label": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"label_name": ["film_tv_&_video"]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_multi/raw/main/dataset/label.multi.json).
```python
{
"arts_&_culture": 0,
"business_&_entrepreneurs": 1,
"celebrity_&_pop_culture": 2,
"diaries_&_daily_life": 3,
"family": 4,
"fashion_&_style": 5,
"film_tv_&_video": 6,
"fitness_&_health": 7,
"food_&_dining": 8,
"gaming": 9,
"learning_&_educational": 10,
"music": 11,
"news_&_social_concern": 12,
"other_hobbies": 13,
"relationships": 14,
"science_&_technology": 15,
"sports": 16,
"travel_&_adventure": 17,
"youth_&_student_life": 18
}
```
### Data Splits
### Citation Information
```
TBA
``` |