Datasets:

cardiffnlp
/

tweet_topic_single

	---
	language:
	- en
	license:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 1k<10K
	task_categories:
	- text-classification
	task_ids:
	- sentiment-classification
	pretty_name: TweetTopicSingle
	---

	# Dataset Card for "cardiffnlp/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
	This is the official repository of TweetTopic (["Twitter Topic Classification
	, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 6 labels.
	Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
	See [cardiffnlp/tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) for multi label version of TweetTopic.
	The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
	The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.

	### Preprocessing
	We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
	For verified usernames, we replace its display name (or account name) with symbols `{@}`.
	For example, a tweet
	```
	Get the all-analog Classic Vinyl Edition
	of "Takin' Off" Album from @herbiehancock
	via @bluenoterecords link below:
	http://bluenote.lnk.to/AlbumOfTheWeek
	```
	is transformed into the following text.
	```
	Get the all-analog Classic Vinyl Edition
	of "Takin' Off" Album from {@herbiehancock@}
	via {@bluenoterecords@} link below: {{URL}}
	```
	A simple function to format tweet follows below.
	```python
	import re
	from urlextract import URLExtract
	extractor = URLExtract()
	def format_tweet(tweet):
	# mask web urls
	urls = extractor.find_urls(tweet)
	for url in urls:
	tweet = tweet.replace(url, "{{URL}}")
	# format twitter account
	tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
	return tweet
	target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
	target_format = format_tweet(target)
	print(target_format)
	'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
	```


	### Data Splits

	\| split \| number of texts \| description \|
	\|:------------------------\|-----:\|------:\|
	\| test_2020 \| 376 \| test dataset from September 2019 to August 2020 \|
	\| test_2021 \| 1693 \| test dataset from September 2020 to August 2021 \|
	\| train_2020 \| 2858 \| training dataset from September 2019 to August 2020 \|
	\| train_2021 \| 1516 \| training dataset from September 2020 to August 2021 \|
	\| train_all \| 4374 \| combined training dataset of `train_2020` and `train_2021` \|
	\| validation_2020 \| 352 \| validation dataset from September 2019 to August 2020 \|
	\| validation_2021 \| 189 \| validation dataset from September 2020 to August 2021 \|
	\| train_random \| 2830 \| randomly sampled training dataset with the same size as `train_2020` from `train_all` \|
	\| validation_random \| 354 \| randomly sampled training dataset with the same size as `validation_2020` from `validation_all` \|
	\| test_coling2022_random \| 3399 \| random split used in the COLING 2022 paper \|
	\| train_coling2022_random \| 3598 \| random split used in the COLING 2022 paper \|
	\| test_coling2022 \| 3399 \| temporal split used in the COLING 2022 paper \|
	\| train_coling2022 \| 3598 \| temporal split used in the COLING 2022 paper \|

	For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
	In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.

	IMPORTANT NOTE: To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).

	### Models

	\| model \| training data \| F1 \| F1 (macro) \| Accuracy \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\|---------:\|-------------:\|-----------:\|
	\| [cardiffnlp/roberta-large-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-all) \| all (2020 + 2021) \| 0.896043 \| 0.800061 \| 0.896043 \|
	\| [cardiffnlp/roberta-base-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-all) \| all (2020 + 2021) \| 0.887773 \| 0.79793 \| 0.887773 \|
	\| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all) \| all (2020 + 2021) \| 0.892499 \| 0.774494 \| 0.892499 \|
	\| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all) \| all (2020 + 2021) \| 0.890136 \| 0.776025 \| 0.890136 \|
	\| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all) \| all (2020 + 2021) \| 0.894861 \| 0.800952 \| 0.894861 \|
	\| [cardiffnlp/roberta-large-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-2020) \| 2020 only \| 0.878913 \| 0.70565 \| 0.878913 \|
	\| [cardiffnlp/roberta-base-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-2020) \| 2020 only \| 0.868281 \| 0.729667 \| 0.868281 \|
	\| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020) \| 2020 only \| 0.882457 \| 0.740187 \| 0.882457 \|
	\| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020) \| 2020 only \| 0.87596 \| 0.746275 \| 0.87596 \|
	\| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020) \| 2020 only \| 0.877732 \| 0.746119 \| 0.877732 \|

	Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py).

	## 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
	}
	```

	### 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"
	}
	```