---
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:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6


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

## 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

| split                       | number of texts | description |
|:----------------------------|-----:|:-----|
| `test`                      | 1693 | alias of `temporal_2021_test` |
| `train`                     | 2858 | alias of `temporal_2020_train` | 
| `validation`                |  352 | alias of `temporal_2020_validation` |
| `temporal_2020_test`        |  376 | test set in 2020 period of temporal split |
| `temporal_2021_test`        | 1693 | test set in 2021 period of temporal split |
| `temporal_2020_train`       | 2858 | training set in 2020 period of temporal split |
| `temporal_2021_train`       | 1516 | training set in 2021 period of temporal split |
| `temporal_2020_validation`  |  352 | validation set in 2020 period of temporal split |
| `temporal_2021_validation`  |  189 | validation set in 2021 period of temporal split |
| `random_train`              | 2830 | training set of random split (mix of 2020 and 2021) |
| `random_validation`         |  354 | validation set of random split (mix of 2020 and 2021) |
| `coling2022_random_test`    | 3399 | test set of random split used in COLING 2022 Tweet Topic paper |
| `coling2022_random_train`   | 3598 | training set of random split used in COLING 2022 Tweet Topic paper |
| `coling2022_temporal_test`  | 3399 | test set of temporal split used in COLING 2022 Tweet Topic paper |
| `coling2022_temporal_train` | 3598 | training set of temporal split used in COLING 2022 Tweet Topic paper|

For the temporal-shift setting, we recommend to train models on `train` (an alias of `temporal_2020_train`) with `validation` (an alias of `temporal_2020_validation`) and evaluate on `test` (an alias of `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`).

**IMPORTANT NOTE:** 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 (the coling2022 split does not have validation set).

### Citation Information

```
@inproceedings{dimosthenis-etal-2022-twitter,
    title = "{T}witter {T}opic {C}lassification",
    author = "Antypas, Dimosthenis  and
    Ushio, Asahi  and
    Camacho-Collados, Jose  and
    Neves, Leonardo  and
    Silva, Vitor  and
    Barbieri, Francesco",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics"
}
```