README.md

---
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
```