Dataset Card for cardiffnlp/tweet_sentiment_multilingual

Dataset Summary

Tweet Sentiment Multilingual consists of sentiment analysis dataset on Twitter in 8 different lagnuages.

• arabic
• english
• french
• german
• hindi
• italian
• portuguese
• spanish

Supported Tasks and Leaderboards

• text_classification: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.

Dataset Structure

Data Instances

An instance from sentiment config:

{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}

Data Fields

For sentiment config:

• text: a string feature containing the tweet.

• label: an int classification label with the following mapping:

  0: negative

  1: neutral

  2: positive

Data Splits

• arabic
• english
• french
• german
• hindi
• italian
• portuguese
• spanish

name	train	validation	test
arabic	1838	323	869
english	1838	323	869
french	1838	323	869
german	1838	323	869
hindi	1838	323	869
italian	1838	323	869
portuguese	1838	323	869
spanish	1838	323	869

Dataset Curators

Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.

Licensing Information

Creative Commons Attribution 3.0 Unported License, and all of the datasets require complying with Twitter Terms Of Service and Twitter API Terms Of Service

Citation Information

@inproceedings{barbieri-etal-2022-xlm,
    title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
    author = "Barbieri, Francesco  and
      Espinosa Anke, Luis  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.27",
    pages = "258--266",
    abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.",
}