
# 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.
task_categories:text-classification task_ids:sentiment-classification task_ids:topic-classification annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:apc language:ajp license:other arxiv:1906.01830 region:us
{"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}}
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-topic-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Levantine Arabic #language-South Levantine Arabic #license-other #arxiv-1906.01830 #region-us 


# Dataset Card for ArSenTD-LEV

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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

- Homepage: ArSenTD-LEV homepage
- Paper: ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets

### 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



### 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

Make sure to read and agree to the license





### Contributions

Thanks to @moussaKam for adding this dataset.
# Dataset Card for ArSenTD-LEV ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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

- Homepage: ArSenTD-LEV homepage
- Paper: ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets ### 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 ### 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

Make sure to read and agree to the license ### Contributions

Thanks to @moussaKam for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-topic-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Levantine Arabic #language-South Levantine Arabic #license-other #arxiv-1906.01830 #region-us 
 # Dataset Card for ArSenTD-LEV ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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

- Homepage: ArSenTD-LEV homepage
- Paper: ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets ### 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 ### 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

Make sure to read and agree to the license ### Contributions

Thanks to @moussaKam for adding this dataset.