---
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:** 19


### Dataset Summary
Topic classification dataset on Twitter with multiple labels per tweet. See [cardiffnlp/tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single) for single label version of Tweet Topic.

## 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 | description |
|:--------------------------|-----:|:-----|
| test                      | 1679 | alias of `temporal_2021_test` |
| train                     | 1505 | alias of `temporal_2020_train` | 
| validation                |  188 | alias of `temporal_2020_validation` |
| temporal_2020_test        |  573 | test set in 2020 period of temporal split |
| temporal_2021_test        | 1679 | test set in 2021 period of temporal split |
| temporal_2020_train       | 4585 | training set in 2020 period of temporal split |
| temporal_2021_train       | 1505 | training set in 2021 period of temporal split |
| temporal_2020_validation  |  573 | validation set in 2020 period of temporal split |
| temporal_2021_validation  |  188 | validation set in 2021 period of temporal split |
| random_train              | 4564 | training set of random split (mix of 2020 and 2021) |
| random_validation         |  573 | validation set of random split (mix of 2020 and 2021) |
| coling2022_random_test    | 5536 | test set of random split used in COLING 2022 Tweet Topic paper |
| coling2022_random_train   | 5731 | training set of random split used in COLING 2022 Tweet Topic paper |
| coling2022_temporal_test  | 5536 | test set of temporal split used in COLING 2022 Tweet Topic paper |
| coling2022_temporal_train | 5731 | training set of temporal split used in COLING 2022 Tweet Topic paper|

For the temporal-shift setting, we recommend to train models on `train` (`temporal_2020_train`) with `validation` (`temporal_2020_validation`) and evaluate on `test` (`temporal_2021_test`).
For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`).
To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `coling2022_temporal_train` and `coling2022_temporal_test` for temporal-shift, and `coling2022_random_train` and `coling2022_temporal_test` fir random split (note that the coling2022 split does not have validation set).


### Citation Information

```
TBA
```