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--- |
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language: |
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- en |
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license: |
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- other |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1k<10K |
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task_categories: |
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- text-classification |
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task_ids: |
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- sentiment-classification |
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pretty_name: TweetTopicSingle |
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--- |
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# Dataset Card for "cardiff_nlp/tweet_topic_multi" |
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## Dataset Description |
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- **Paper:** TBA |
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- **Dataset:** Tweet Topic Dataset |
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- **Domain:** Twitter |
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- **Number of Class:** 19 |
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### Dataset Summary |
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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. |
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## Dataset Structure |
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### Data Instances |
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An example of `train` looks as follows. |
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```python |
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{ |
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"date": "2021-03-07", |
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"text": "The latest The Movie theater Daily! {{URL}} Thanks to {{USERNAME}} {{USERNAME}} {{USERNAME}} #lunchtimeread #amc1000", |
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"id": "1368464923370676231", |
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"label": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
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"label_name": ["film_tv_&_video"] |
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} |
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``` |
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### Label ID |
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The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_multi/raw/main/dataset/label.multi.json). |
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```python |
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{ |
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"arts_&_culture": 0, |
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"business_&_entrepreneurs": 1, |
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"celebrity_&_pop_culture": 2, |
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"diaries_&_daily_life": 3, |
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"family": 4, |
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"fashion_&_style": 5, |
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"film_tv_&_video": 6, |
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"fitness_&_health": 7, |
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"food_&_dining": 8, |
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"gaming": 9, |
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"learning_&_educational": 10, |
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"music": 11, |
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"news_&_social_concern": 12, |
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"other_hobbies": 13, |
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"relationships": 14, |
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"science_&_technology": 15, |
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"sports": 16, |
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"travel_&_adventure": 17, |
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"youth_&_student_life": 18 |
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} |
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``` |
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### Data Splits |
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| split | number of texts | description | |
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|:----------------------------|-----:|:-----| |
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| `test` | 1679 | alias of `temporal_2021_test` | |
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| `train` | 4585 | alias of `temporal_2020_train` | |
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| `validation` | 573 | alias of `temporal_2020_validation` | |
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| `temporal_2020_test` | 573 | test set in 2020 period of temporal split | |
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| `temporal_2021_test` | 1679 | test set in 2021 period of temporal split | |
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| `temporal_2020_train` | 4585 | training set in 2020 period of temporal split | |
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| `temporal_2021_train` | 1505 | training set in 2021 period of temporal split | |
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| `temporal_2020_validation` | 573 | validation set in 2020 period of temporal split | |
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| `temporal_2021_validation` | 188 | validation set in 2021 period of temporal split | |
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| `random_train` | 4564 | training set of random split (mix of 2020 and 2021) | |
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| `random_validation` | 573 | validation set of random split (mix of 2020 and 2021) | |
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| `coling2022_random_test` | 5536 | test set of random split used in COLING 2022 Tweet Topic paper | |
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| `coling2022_random_train` | 5731 | training set of random split used in COLING 2022 Tweet Topic paper | |
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| `coling2022_temporal_test` | 5536 | test set of temporal split used in COLING 2022 Tweet Topic paper | |
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| `coling2022_temporal_train` | 5731 | training set of temporal split used in COLING 2022 Tweet Topic paper| |
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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`). |
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For the random split, we recommend to train models on `random_train` with `random_validation` and evaluate on `test` (`temporal_2021_test`). |
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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 (note that the coling2022 split does not have validation set). |
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### Citation Information |
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``` |
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TBA |
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``` |