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_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
```