ar_res_reviews / README.md
system's picture
system HF staff
Update files from the datasets library (from 1.2.0)
b8efa50
|
raw
history blame
4.23 kB
---
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:
- sentiment-classification
---
# Dataset Card for ArRestReviews
## 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:** [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces)
- **Repository:** [Large Arabic Sentiment Analysis Resources](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces)
- **Paper:** [ Building Large Arabic Multi-domain Resources for Sentiment Analysis](https://github.com/hadyelsahar/large-arabic-sentiment-analysis-resouces/blob/master/Paper%20-%20Building%20Large%20Arabic%20Multi-domain%20Resources%20for%20Sentiment%20Analysis.pdf)
- **Point of Contact:** [hady elsahar](hadyelsahar@gmail.com)
### Dataset Summary
Dataset of 8364 restaurant reviews from qaym.com in Arabic for sentiment analysis
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises of the following:
- "polarity": which is a string value of either 0 or 1 indicating the sentiment around the review
- "text": is the review plain text of a restaurant in Arabic
- "restaurant_id": the restaurant ID on the website
- "user_id": the user ID on the website
example:
```
{
'polarity': 0, # negative
'restaurant_id': '1412',
'text': 'عادي جدا مامن زود',
'user_id': '21294'
}
```
### Data Fields
- "polarity": is a string value of either 0 or 1 indicating the sentiment around the review
- "text": is the review plain text of a restaurant in Arabic
- "restaurant_id": the restaurant ID on the website (string)
- "user_id": the user ID on the website (string)
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Contains 8364 restaurant reviews from qaym.com
#### Who are the source language producers?
From tweeter.
### Annotations
The polarity field provides a label of 1 or -1 pertaining to the sentiment of the review
#### 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
@InProceedings{10.1007/978-3-319-18117-2_2,
author="ElSahar, Hady
and El-Beltagy, Samhaa R.",
editor="Gelbukh, Alexander",
title="Building Large Arabic Multi-domain Resources for Sentiment Analysis",
booktitle="Computational Linguistics and Intelligent Text Processing",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="23--34",
isbn="978-3-319-18117-2"
}