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Dataset Card for "cardiffnlp/tweet_topic_multi"
Dataset Summary
This is the official repository of TweetTopic ("Twitter Topic Classification , COLING main conference 2022"), 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 for single label version of TweetTopic. The tweet collection used in TweetTopic is same as what used in TweetNER7. The dataset is integrated in TweetNLP 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.
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 fine-tuning script can be found here.
Dataset Structure
Data Instances
An example of train
looks as follows.
{
"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.
The label2id dictionary can be found here.
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"
}
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