README.md

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: TBA
• Dataset: Tweet Topic Dataset
• Domain: Twitter
• Number of Class: 6

Dataset Summary

Topic classification dataset on Twitter with multiple labels per tweet.

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
test	1679
train	1505
validation	188
temporal_2020_test	573
temporal_2021_test	1679
temporal_2020_train	4585
temporal_2021_train	1505
temporal_2020_validation	573
temporal_2021_validation	188
random_train	4564
random_validation	573
coling2022_random_test	5536
coling2022_random_train	5731
coling2022_temporal_test	5536
coling2022_temporal_train	5731

Citation Information

TBA