|
--- |
|
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"] |
|
} |
|
``` |
|
|
|
### Label ID |
|
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_multi/raw/main/dataset/label.multi.json). |
|
```python |
|
{ |
|
"arts_&_culture": 0, |
|
"business_&_entrepreneurs": 1, |
|
"celebrity_&_pop_culture": 2, |
|
"diaries_&_daily_life": 3, |
|
"family": 4, |
|
"fashion_&_style": 5, |
|
"film_tv_&_video": 6, |
|
"fitness_&_health": 7, |
|
"food_&_dining": 8, |
|
"gaming": 9, |
|
"learning_&_educational": 10, |
|
"music": 11, |
|
"news_&_social_concern": 12, |
|
"other_hobbies": 13, |
|
"relationships": 14, |
|
"science_&_technology": 15, |
|
"sports": 16, |
|
"travel_&_adventure": 17, |
|
"youth_&_student_life": 18 |
|
} |
|
``` |
|
|
|
|
|
### 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" |
|
} |
|
``` |
