--- 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_multi" ## Dataset Description - **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824) - **Dataset:** Tweet Topic Dataset - **Domain:** Twitter - **Number of Class:** 19 ### 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 19 labels. Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021. See [cardiffnlp/tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single) for single 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 | 573 | test dataset from September 2019 to August 2020 | | test_2021 | 1679 | test dataset from September 2020 to August 2021 | | train_2020 | 4585 | training dataset from September 2019 to August 2020 | | train_2021 | 1505 | training dataset from September 2020 to August 2021 | | train_all | 6090 | combined training dataset of `train_2020` and `train_2021` | | validation_2020 | 573 | validation dataset from September 2019 to August 2020 | | validation_2021 | 188 | validation dataset from September 2020 to August 2021 | | train_random | 4564 | randomly sampled training dataset with the same size as `train_2020` from `train_all` | | validation_random | 573 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` | | test_coling2022_random | 5536 | random split used in the COLING 2022 paper | | train_coling2022_random | 5731 | random split used in the COLING 2022 paper | | test_coling2022 | 5536 | temporal split used in the COLING 2022 paper | | train_coling2022 | 5731 | 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-multi-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-multi-all) | all (2020 + 2021) | 0.763104 | 0.620257 | 0.536629 | | [cardiffnlp/roberta-base-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-multi-all) | all (2020 + 2021) | 0.751814 | 0.600782 | 0.531864 | | [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all) | all (2020 + 2021) | 0.762513 | 0.603533 | 0.547945 | | [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all) | all (2020 + 2021) | 0.759917 | 0.59901 | 0.536033 | | [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all) | all (2020 + 2021) | 0.764767 | 0.618702 | 0.548541 | | [cardiffnlp/roberta-large-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-multi-2020) | 2020 only | 0.732366 | 0.579456 | 0.493746 | | [cardiffnlp/roberta-base-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-multi-2020) | 2020 only | 0.725229 | 0.561261 | 0.499107 | | [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020) | 2020 only | 0.73671 | 0.565624 | 0.513401 | | [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020) | 2020 only | 0.729446 | 0.534799 | 0.50268 | | [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020) | 2020 only | 0.731106 | 0.532141 | 0.509827 | Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/lm_finetuning.py). ## Dataset Structure ### Data Instances An example of `train` looks as follows. ```python { "date": "2021-03-07", "text": "The latest The Movie theater Daily! {{URL}} Thanks to {{USERNAME}} {{USERNAME}} {{USERNAME}} #lunchtimeread #amc1000", "id": "1368464923370676231", "label": [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "label_name": ["film_tv_&_video"] } ``` ### Labels | 0: arts_&_culture | 5: fashion_&_style | 10: learning_&_educational | 15: science_&_technology | |-----------------------------|---------------------|----------------------------|--------------------------| | 1: business_&_entrepreneurs | 6: film_tv_&_video | 11: music | 16: sports | | 2: celebrity_&_pop_culture | 7: fitness_&_health | 12: news_&_social_concern | 17: travel_&_adventure | | 3: diaries_&_daily_life | 8: food_&_dining | 13: other_hobbies | 18: youth_&_student_life | | 4: family | 9: gaming | 14: relationships | | Annotation instructions can be found [here](https://docs.google.com/document/d/1IaIXZYof3iCLLxyBdu_koNmjy--zqsuOmxQ2vOxYd_g/edit?usp=sharing). The label2id dictionary can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi/blob/main/dataset/label.multi.json). ### 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" } ```