license: mit
task_categories:
- text-classification
language:
- ro
pretty_name: REDv2
size_categories:
- 1K<n<10K
Dataset Card for [REDv2]
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
This is the second version of the Romanian Emotions Dataset (RED) containing 5449 tweets annotated in a multi-label fashion with the following 7 emotions: Anger (Furie), Fear (Frică), Joy (Bucurie), Sadness (Tristețe), Surprise (Surpriză), Trust (Încredere) and Neutral (Neutru).
Supported Tasks and Leaderboards
This dataset is intended for multi-class & multi-label emotion classification.
Languages
The data is in Romanian.
Dataset Structure
Data Instances
Each instance is a tweet with a corresponding ID and one or more emotion annotations (or neutral).
Data Fields
The simplified configuration includes:
text: the tweet
text_id: unique identifier of the tweet (can be used to look up the entry in the raw dataset)
agreed_labels: the agreed emotion annotations vector (each value of 1 means that at least two annotators recognized that specific emotion)
procentual_labels: vector containing three values: 0.33 if one annotator recognised the emotion, 0.66 if two annotators agreed on the emotion, and 0.99 if all annotators recognised the emotion
In addition to the above, the raw data includes:
Anger, Fear, Joy, Neutral, Sadness, Surprise, Trust: boolean values - True if the specific emotion is found in the agreed_labels vector
annotator1, annotator2, annotator3: vectors of zeros of ones - 1 means the annotator recognized the emotion on the corresponding vector index
sum_labels: the sum of annotator1, annotator2 and annotator3 vectors
The arrays of 7 values correspond to the following emotions: ['Sadness', 'Surprise', 'Fear', 'Anger', 'Neutral', 'Trust', 'Joy'].
Data Splits
This dataset includes a set of train/val/test splits with 4088, 818, and 543 examples respectively.
Dataset Creation
Curation Rationale
From the paper introduction:
Interpreting correctly one’s own emotions, as well as other people’s emotional states, is a central aspect of emotional intelligence. Today, people can automate the process of emotion detection by creating machine learning models, provided by the fact that the model training was done on qualitative and sufficient data. With the constant increase of social media usage there is also an increase in online public data, freely available for model creation. Thus, analyzing emotions in online content naturally has became more and more of a topic of interest in the recent years.
Source Data
Initial Data Collection and Normalization
Data was collected from Twitter (for more information see Chapter 3.1 of the paper).
Who are the source language producers?
Romanian-speaking Twitter users.
Annotations
Annotation process
See Chapter 3.2. in the paper.
Who are the annotators?
Annotations were produced by 66 Cognitive Science students, University of Bucharest, Faculty of Psichology and Educational Sciences.
Personal and Sensitive Information
All tweets in this dataset are anonymized by removing usernames and proper nouns.
Additional Information
Dataset Curators
Researchers at the University of Bucharest and Adobe (see the authors of the paper here).
Licensing Information
The GitHub repository of this dataset has an MIT license.
Citation Information
If you are using this dataset in your research, please cite:
@inproceedings{redv2,
author = "Alexandra Ciobotaru and
Mihai V. Constantinescu and
Liviu P. Dinu and
Stefan Daniel Dumitrescu",
title = "{RED} v2: {E}nhancing {RED} {D}ataset for {M}ulti-{L}abel {E}motion {D}etection",
journal = "Proceedings of the 13th Language Resources and Evaluation Conference (LREC 2022)",
pages = "1392–1399",
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.149.pdf",
language = "English"
}
Contributions
Thanks to @Alegzandra for adding this dataset.