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Datasets:
cardiffnlp
/
tweet_topic_multi

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
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tweet_topic_multi
File size: 2,252 Bytes
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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.

## 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


| split                     | number of texts |
|:--------------------------|-----:|
| test                      | 1679 |
| train                     | 1505 |
| validation                |  188 |
| temporal_2020_test        |  573 |
| temporal_2021_test        | 1679 |
| temporal_2020_train       | 4585 |
| temporal_2021_train       | 1505 |
| temporal_2020_validation  |  573 |
| temporal_2021_validation  |  188 |
| random_train              | 4564 |
| random_validation         |  573 |
| coling2022_random_test    | 5536 |
| coling2022_random_train   | 5731 |
| coling2022_temporal_test  | 5536 |
| coling2022_temporal_train | 5731 |



### Citation Information

```
TBA
```