metadata

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
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"), 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

TBA

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"]
}

Label ID

The label2id dictionary can be found at here.

{
    "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"
}