--- 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" } ```