dianalogan/Marketing-Budget-and-Actual-Sales-Dataset

Dataset Card for tweet_eval

Dataset Summary

TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks 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 emoji config:

{'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'}

An instance from emotion config:

{'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer.  #motivation #leadership #worry"}

An instance from hate config:

{'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'}

An instance from irony config:

{'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'}

An instance from offensive config:

{'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'}

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"'}

An instance from stance_abortion config:

{'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'}

An instance from stance_atheism config:

{'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'}

An instance from stance_climate config:

{'label': 0, 'text': 'Why Is The Pope Upset?  via @user #UnzippedTruth #PopeFrancis #SemST'}

An instance from stance_feminist config:

{'label': 1, 'text': "@user @user is the UK's answer to @user and @user  #GamerGate #SemST"}

An instance from stance_hillary config:

{'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"}

Data Fields

For emoji config:

• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: ❤ 1: 😍 2: 😂 3: 💕 4: 🔥 5: 😊 6: 😎 7: ✨ 8: 💙 9: 😘 10: 📷 11: 🇺🇸 12: ☀ 13: 💜 14: 😉 15: 💯 16: 😁 17: 🎄 18: 📸 19: 😜 For emotion config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: anger 1: joy 2: optimism 3: sadness For hate config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: non-hate 1: hate For irony config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: non_irony 1: irony For offensive config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: non-offensive 1: offensive 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 For stance_abortion config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: none 1: against 2: favor For stance_atheism config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: none 1: against 2: favor For stance_climate config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: none 1: against 2: favor For stance_feminist config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: none 1: against 2: favor For stance_hillary config:
• text: a string feature containing the tweet.
• label: an int classification label with the following mapping: 0: none 1: against 2: favor

Data Splits

name	train	validation	test
emoji	45000	5000	50000
emotion	3257	374	1421
hate	9000	1000	2970
irony	2862	955	784
offensive	11916	1324	860
sentiment	45615	2000	12284
stance_abortion	587	66	280
stance_atheism	461	52	220
stance_climate	355	40	169
stance_feminist	597	67	285
stance_hillary	620	69	295

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 is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions). All of the datasets require complying with Twitter Terms Of Service and Twitter API Terms Of Service Additionally the license are:

• emoji: Undefined
• emotion(EmoInt): Undefined
• hate (HateEval): Need permission here
• irony: Undefined
• Offensive: Undefined
• Sentiment: Creative Commons Attribution 3.0 Unported License
• Stance: Undefined

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}
}

If you use any of the TweetEval datasets, please cite their original publications:

Emotion Recognition:

@inproceedings{mohammad2018semeval,
  title={Semeval-2018 task 1: Affect in tweets},
  author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  booktitle={Proceedings of the 12th international workshop on semantic evaluation},
  pages={1--17},
  year={2018}
}

Emoji Prediction:

@inproceedings{barbieri2018semeval,
  title={Semeval 2018 task 2: Multilingual emoji prediction},
  author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and
    Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio},
  booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
  pages={24--33},
  year={2018}
}

Irony Detection:

@inproceedings{van2018semeval,
  title={Semeval-2018 task 3: Irony detection in english tweets},
  author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique},
  booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation},
  pages={39--50},
  year={2018}
}

Hate Speech Detection:

@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and Bosco, Cristina  and Fersini, Elisabetta  and Nozza, Debora and Patti, Viviana and
      Rangel Pardo, Francisco Manuel  and Rosso, Paolo  and Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2007",
    doi = "10.18653/v1/S19-2007",
    pages = "54--63"
}

Offensive Language Identification:

@inproceedings{zampieri2019semeval,
  title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
  author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh},
  booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
  pages={75--86},
  year={2019}
}

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}
}

Stance Detection:

@inproceedings{mohammad2016semeval,
  title={Semeval-2016 task 6: Detecting stance in tweets},
  author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin},
  booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
  pages={31--41},
  year={2016}
}