README.md

metadata

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

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.

	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, @lhoestq for adding this dataset.