Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 1,972 Bytes
29fd70e 4a40752 29fd70e 4a40752 29fd70e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
---
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:** TBA
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6
### Dataset Summary
Topic classification dataset on Twitter with single label per tweet.
## 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 |
|:--------------------------|-----:|
| 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
``` |