ramybaly/arsentd_lev

---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- apc
- ajp
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- topic-classification
paperswithcode_id: arsentd-lev
pretty_name: ArSenTD-LEV
dataset_info:
features:
- name: Tweet
dtype: string
- name: Country
dtype:
class_label:
names:
'0': jordan
'1': lebanon
'2': syria
'3': palestine
- name: Topic
dtype: string
- name: Sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
'3': very_negative
'4': very_positive
- name: Sentiment_Expression
dtype:
class_label:
names:
'0': explicit
'1': implicit
'2': none
- name: Sentiment_Target
dtype: string
splits:
- name: train
num_bytes: 1233980
num_examples: 4000
download_size: 392666
dataset_size: 1233980
---

# Dataset Card for ArSenTD-LEV

## 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:** [ArSenTD-LEV homepage](http://oma-project.com/)
- **Paper:** [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830)

### Dataset Summary

The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria.

### Supported Tasks and Leaderboards

Sentriment analysis

### Languages

Arabic Levantine Dualect

## Dataset Structure

### Data Instances

{'Country': 0,
'Sentiment': 3,
'Sentiment_Expression': 0,
'Sentiment_Target': 'هاي سوالف عصابات ارهابية',
'Topic': 'politics',
'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'}

### Data Fields

`Tweet`: the text content of the tweet \
`Country`: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')\
`Topic`: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others) \
`Sentiment`: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive) \
`Sentiment_Expression`: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral) \
`Sentiment_Target`: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value.

### Data Splits

No standard splits are provided

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### 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

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

Make sure to read and agree to the [license](http://oma-project.com/ArSenL/ArSenTD_Lev_Intro)

### Citation Information

```
@article{baly2019arsentd,
title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets},
author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir},
journal={arXiv preprint arXiv:1906.01830},
year={2019}
}
```

### Contributions

Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.

原始信息汇总

数据集概述

数据集基本信息

• 名称: ArSenTD-LEV
• 语言: 阿拉伯语黎凡特方言
• 许可证: 其他
• 多语言性: 单语
• 大小: 1K<n<10K
• 来源: 原始数据
• 任务类别: 文本分类
• 任务ID: 情感分类, 主题分类

数据集结构

数据字段

• Tweet: 字符串类型
• Country: 类别标签，包括约旦、黎巴嫩、叙利亚、巴勒斯坦
• Topic: 字符串类型，包括个人、政治、宗教、体育、娱乐等
• Sentiment: 类别标签，包括非常负面、负面、中性、正面、非常正面
• Sentiment_Expression: 类别标签，包括明确、隐含、无
• Sentiment_Target: 字符串类型

数据分割

• 训练集: 4000个样本，总大小1233980字节

数据集创建

许可证信息

• 使用前需阅读并同意相关许可证

引用信息

@article{baly2019arsentd, title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets}, author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir}, journal={arXiv preprint arXiv:1906.01830}, year={2019} }