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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:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **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` | 4585 | alias of `temporal_2020_train` |
| `validation` | 573 | 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` (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"
}
``` |