Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
| 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. 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 | |
| ``` | |
| TBA | |
| ``` |