README.md

metadata

annotations_creators:
  - found
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other-tweet-datasets
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
  - sentiment-classification
paperswithcode_id: tweeteval
pretty_name: TweetEval
config_names:
  - sentiment
dataset_info:
  - config_name: sentiment
    features:
      - name: text
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': negative
              '1': neutral
              '2': positive
    splits:
      - name: train
        num_bytes: 5425122
        num_examples: 45615
      - name: test
        num_bytes: 1279540
        num_examples: 12284
      - name: validation
        num_bytes: 239084
        num_examples: 2000
    download_size: 4849675
    dataset_size: 6943746
configs:
  - config_name: sentiment
    data_files:
      - split: train
        path: sentiment/train-*
      - split: test
        path: sentiment/test-*
      - split: validation
        path: sentiment/validation-*
train-eval-index:
  - config: sentiment
    task: text-classification
    task_id: multi_class_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      text: text
      label: target
    metrics:
      - type: accuracy
        name: Accuracy
      - type: f1
        name: F1 macro
        args:
          average: macro
      - type: f1
        name: F1 micro
        args:
          average: micro
      - type: f1
        name: F1 weighted
        args:
          average: weighted
      - type: precision
        name: Precision macro
        args:
          average: macro
      - type: precision
        name: Precision micro
        args:
          average: micro
      - type: precision
        name: Precision weighted
        args:
          average: weighted
      - type: recall
        name: Recall macro
        args:
          average: macro
      - type: recall
        name: Recall micro
        args:
          average: micro
      - type: recall
        name: Recall weighted
        args:
          average: weighted

Dataset Card for tweet_eval

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: [Needs More Information]
• Repository: GitHub
• Paper: EMNLP Paper
• Leaderboard: GitHub Leaderboard
• Point of Contact: [Needs More Information]

Dataset Summary

TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. This configuration exposes the sentiment task only. All datasets have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits.

Supported Tasks and Leaderboards

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

Languages

The text in the dataset is in English, as spoken by Twitter users.

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

name	train	validation	test
sentiment	45615	2000	12284

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

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

Licensing Information

This dataset requires complying with Twitter Terms Of Service and Twitter API Terms Of Service

Sentiment license: Creative Commons Attribution 3.0 Unported License

Citation Information

@inproceedings{barbieri2020tweeteval,
title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}},
author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo},
booktitle={Proceedings of Findings of EMNLP},
year={2020}
}

Sentiment Analysis:

@inproceedings{rosenthal2017semeval,
  title={SemEval-2017 task 4: Sentiment analysis in Twitter},
  author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav},
  booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)},
  pages={502--518},
  year={2017}
}

Contributions

Thanks to @gchhablani and @abhishekkrthakur for adding this dataset.