Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
ArXiv:
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File size: 4,211 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_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"
}
``` |