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 "cardiff_nlp/tweet_topic_multi"

Dataset Description

Paper: https://arxiv.org/abs/2209.09824
Dataset: Tweet Topic Dataset
Domain: Twitter
Number of Class: 19

Dataset Summary

Topic classification dataset on Twitter with multiple labels per tweet. See cardiffnlp/tweet_topic_single for single label version of Tweet Topic.

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
 }

Data Splits

split	number of texts	description
`test`	1679	alias of `temporal_2021_test`
`train`	4585	alias of `temporal_2020_train`
`validation`	573	alias of `temporal_2020_validation`
`temporal_2020_test`	573	test set in 2020 period of temporal split
`temporal_2021_test`	1679	test set in 2021 period of temporal split
`temporal_2020_train`	4585	training set in 2020 period of temporal split
`temporal_2021_train`	1505	training set in 2021 period of temporal split
`temporal_2020_validation`	573	validation set in 2020 period of temporal split
`temporal_2021_validation`	188	validation set in 2021 period of temporal split
`random_train`	4564	training set of random split (mix of 2020 and 2021)
`random_validation`	573	validation set of random split (mix of 2020 and 2021)
`coling2022_random_test`	5536	test set of random split used in COLING 2022 Tweet Topic paper
`coling2022_random_train`	5731	training set of random split used in COLING 2022 Tweet Topic paper
`coling2022_temporal_test`	5536	test set of temporal split used in COLING 2022 Tweet Topic paper
`coling2022_temporal_train`	5731	training set of temporal split used in COLING 2022 Tweet Topic paper

For the temporal-shift setting, we recommend to train models on train (an alias of temporal_2020_train) with validation (an alias of temporal_2020_validation) and evaluate on test (an alias of temporal_2021_test). For the random split, we recommend to train models on random_train with random_validation and evaluate on test (temporal_2021_test).

IMPORTANT NOTE: To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use coling2022_temporal_train and coling2022_temporal_test for temporal-shift, and coling2022_random_train and coling2022_temporal_test fir random split (the coling2022 split does not have validation set).

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"
}