TextToKids/EmoTextToKids-sentences

Dataset Card for [Dataset Name]

Dataset Summary

EmoTextToKids provides sentences from written documents annotated in emotions. Emotions are characterized by their emotional category (fear, anger, pride...) and their expression mode (labeled, behavioral, displayed or suggester). As opposed to usual datasets in emotion recognition, the documents are not conversational. They are newspapers, encyclopedias, novels, dedicated to children.

Supported Tasks and Leaderboards

• Emotion recognition

Languages

• French

Dataset Structure

Data Instances

{
  "previous_sentence": "Un an plus tard, le Sénat lui accorde la dictature sans limite dans le temps. ",
  "target_sentence": "Mais à Rome, la gloire de César inquiète certains sénateurs. ",
  "next_sentence": "Un complot commence à s’organiser autour d’un homme nommé Cassius. ",
  "is_emotional": true,
  "modes": [
      "labeled"
    ],
  "types": [
      "basic"
    ],
  "categories": [
      "fear"
    ]
}

The fields modes, types and categories are lists because several emotions can be present in a unique sentence.

Data Fields

[More Information Needed]

Data Splits

Subset	Texts	Sent.	Tokens	Emotional sent.
train	1,129	19,553	360K	3,952
dev	182	2,770	53K	438
test	283	5,588	102K	984
Total	1,594	27,911	515K	5,374

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

Data were manually annotated by 6 experts following annotation guidelines here: https://hal.science/hal-03263194 .

Annotations were validated by comparing a significant sample of the annotated data with annotation of an external expert. Below are the kappa coefficients.

Label	Kappa
emotional	0.66
Modes
behavioral	0.70
labeled	0.73
displayed	0.68
suggested	0.46
Types
basic	0.66
complex	0.55
Categories
admiration	0.53
anger	0.71
guilt	0.50
disgust	0.87
embarrassment	0.51
pride	0.25
jealousy	1.00
joy	0.51
fear	0.64

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@misc

@inproceedings{etienne2024emotion,
  title={Emotion Identification for French in Written Texts: Considering Modes of Emotion Expression as a Step Towards Text Complexity Analysis},
  author={{\'E}tienne, Aline and Battistelli, Delphine and Lecorv{\'e}, Gw{\'e}nol{\'e}},
  booktitle={Proceedings of the 14th ACL Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA)},
  year={2024}
}