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README.md

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+# Dataset Card for [REDv2]
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## 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](https://aclanthology.org/2022.lrec-1.149.pdf)).
+
+#### Who are the source language producers?
+
+Romanian-speaking Twitter users.
+
+### Annotations
+
+#### Annotation process
+
+See Chapter 3.2. in the [paper](https://aclanthology.org/2022.lrec-1.149.pdf).
+
+#### 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](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.149.pdf)).
+
+### Licensing Information
+
+The [GitHub repository](https://github.com/Alegzandra/RED-Romanian-Emotions-Dataset/tree/main/REDv2) 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](https://github.com/<Alegzandra>) for adding this dataset.
+
+
+