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Datasets:
emotone-ar-cicling2017
/
emotone_ar

Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Arabic
Size:
10K<n<100K
License:
Dataset card Files Community
3
emotone_ar
File size: 3,916 Bytes 
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---
annotations_creators:
- found
language_creators:
- found
languages:
- ar
licenses:
- unknown
multilinguality:
- monolingual
size_categories:
- 1k<n<10k
source_datasets:
- original
task_categories:
- text_classification
task_ids:
- emotion-classification
---

# Dataset Card for MetRec

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-instances)
  - [Data Splits](#data-instances)
- [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)
  - [Discussion of Social Impact and Biases](#discussion-of-social-impact-and-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)

## Dataset Description

- **Homepage:** [Homepage](https://github.com/AmrMehasseb/Emotional-Tone)
- **Repository:** [Repository](https://github.com/AmrMehasseb/Emotional-Tone)
- **Paper:** [Emotional Tone Detection in Arabic Tweets](https://www.researchgate.net/publication/328164296_Emotional_Tone_Detection_in_Arabic_Tweets_18th_International_Conference_CICLing_2017_Budapest_Hungary_April_17-23_2017_Revised_Selected_Papers_Part_II)
- **Point of Contact:** [Amr Al-Khatib](https://github.com/AmrMehasseb)

### Dataset Summary

Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

The dataset is based on Arabic.

## Dataset Structure

### Data Instances

example: 
```
    >>> {'label': 0, 'tweet': 'ุงู„ุงูˆู„ูŠู…ุจูŠุงุฏ ุงู„ุฌุงูŠู‡ ู‡ูƒูˆู† ู„ุณู‡ ู ุงู„ูƒู„ูŠู‡ ..'}
```

### Data Fields

- "tweet": plain text tweet in Arabic

- "label": emotion class label

the dataset distribution and balance for each class looks like the following

|label||Label description |  Count    |
|---------|---------|  -------  | 
|0        |none     |   1550    |
|1        |anger    |   1444    |
|2        |joy      |   1281    |
|3        |sadness  |   1256    |
|4        |love     |   1220    |
|5        |sympathy |   1062    |
|6        |surprise |   1045    |
|7        |fear     |   1207    |

### Data Splits

The dataset is not split. 

|           | Tain   | 
|---------- | ------ | 
|no split   | 10,065  | 

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations



#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Discussion of Social Impact and Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

@inbook{inbook,
author = {Al-Khatib, Amr and El-Beltagy, Samhaa},
year = {2018},
month = {01},
pages = {105-114},
title = {Emotional Tone Detection in Arabic Tweets: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17โ€“23, 2017, Revised Selected Papers, Part II},
isbn = {978-3-319-77115-1},
doi = {10.1007/978-3-319-77116-8_8}
}