
# Dataset Card for Arabic Jordanian General Tweets

## Table of Contents
- [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets)
  - [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)
  - [|split|num examples|](#splitnum-examples)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [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

- **Repository:** [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT)
- **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66)
- **Point of Contact:** [Khaled Alomari](khaled.alomari@adu.ac.ae)

### Dataset Summary

Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.

### Supported Tasks and Leaderboards

The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66). 

### Languages

The dataset is based on Arabic.

## Dataset Structure

### Data Instances

A binary datset with with negative and positive sentiments.  

### Data Fields

- `text` (str): Tweet text.
- `label` (int): Sentiment.

### Data Splits

The dataset is not split.

|          | train |
|----------|------:|
| no split | 1,800 | 

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

Contains 1,800 tweets collected from twitter. 

#### Who are the source language producers?

From tweeter.  

### Annotations

The dataset does not contain any additional 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

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```
@inproceedings{alomari2017arabic,
  title={Arabic tweets sentimental analysis using machine learning},
  author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
  booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
  pages={602--610},
  year={2017},
  organization={Springer}
}
```

### Contributions

Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
task_categories:text-classification task_ids:sentiment-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:ar license:unknown region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["ar"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "Arabic Jordanian General Tweets", "dataset_info": {"config_name": "plain_text", "features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Positive"}}}}], "splits": [{"name": "train", "num_bytes": 175420, "num_examples": 1800}], "download_size": 91857, "dataset_size": 175420}, "configs": [{"config_name": "plain_text", "data_files": [{"split": "train", "path": "plain_text/train-*"}], "default": true}]}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-unknown #region-us 

Dataset Card for Arabic Jordanian General Tweets
================================================


Table of Contents
-----------------


* Dataset Card for Arabic Jordanian General Tweets
	+ Table of Contents
	+ Dataset Description
		- Dataset Summary
		- Supported Tasks and Leaderboards
		- Languages
	+ Dataset Structure
		- Data Instances
		- Data Fields
		- Data Splits
	+ |split|num examples|
	+ Dataset Creation
		- Curation Rationale
		- Source Data
			* Initial Data Collection and Normalization
			* Who are the source language producers?
		- Annotations
			* Annotation process
			* Who are the annotators?
		- Personal and Sensitive Information
	+ Considerations for Using the Data
		- Social Impact of Dataset
		- Discussion of Biases
		- Other Known Limitations
	+ Additional Information
		- Dataset Curators
		- Licensing Information
		- Citation Information
		- Contributions


Dataset Description
-------------------


* Repository: Arabic Jordanian General Tweets
* Paper: Arabic Tweets Sentimental Analysis Using Machine Learning
* Point of Contact: Khaled Alomari


### Dataset Summary


Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.


### Supported Tasks and Leaderboards


The dataset was published on this paper.


### Languages


The dataset is based on Arabic.


Dataset Structure
-----------------


### Data Instances


A binary datset with with negative and positive sentiments.


### Data Fields


* 'text' (str): Tweet text.
* 'label' (int): Sentiment.


### Data Splits


The dataset is not split.



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


Contains 1,800 tweets collected from twitter.


#### Who are the source language producers?


From tweeter.


### Annotations


The dataset does not contain any additional annotations.


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


Thanks to @zaidalyafeai, @lhoestq for adding this dataset.



### Dataset Summary


Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. ### Supported Tasks and Leaderboards


The dataset was published on this paper. ### Languages


The dataset is based on Arabic.


Dataset Structure
----------------- ### Data Instances


A binary datset with with negative and positive sentiments. ### Data Fields


* 'text' (str): Tweet text.
* 'label' (int): Sentiment. ### Data Splits


The dataset is not split.



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


Contains 1,800 tweets collected from twitter. #### Who are the source language producers?


From tweeter. ### Annotations


The dataset does not contain any additional annotations. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @zaidalyafeai, @lhoestq for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-unknown #region-us 
 ### Dataset Summary


Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. ### Supported Tasks and Leaderboards


The dataset was published on this paper. ### Languages


The dataset is based on Arabic.


Dataset Structure
----------------- ### Data Instances


A binary datset with with negative and positive sentiments. ### Data Fields


* 'text' (str): Tweet text.
* 'label' (int): Sentiment. ### Data Splits


The dataset is not split.



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


Contains 1,800 tweets collected from twitter. #### Who are the source language producers?


From tweeter. ### Annotations


The dataset does not contain any additional annotations. #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @zaidalyafeai, @lhoestq for adding this dataset.