|
--- |
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annotations_creators: |
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- crowdsourced |
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language_creators: |
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- found |
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languages: |
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- apc |
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- apj |
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licenses: |
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- other-Copyright-2018-by-[American-University-of-Beirut] |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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task_categories: |
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- text-classification |
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task_ids: |
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- sentiment-classification |
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- topic-classification |
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--- |
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# Dataset Card for ArSenTD-LEV |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-instances) |
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- [Data Splits](#data-instances) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** [ArSenTD-LEV homepage](http://oma-project.com/) |
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- **Paper:** [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830) |
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### Dataset Summary |
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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. |
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### Supported Tasks and Leaderboards |
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Sentriment analysis |
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### Languages |
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Arabic Levantine Dualect |
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## Dataset Structure |
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### Data Instances |
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{'Country': 0, |
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'Sentiment': 3, |
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'Sentiment_Expression': 0, |
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'Sentiment_Target': 'هاي سوالف عصابات ارهابية', |
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'Topic': 'politics', |
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'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'} |
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### Data Fields |
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`Tweet`: the text content of the tweet \ |
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`Country`: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')\ |
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`Topic`: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others) \ |
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`Sentiment`: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive) \ |
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`Sentiment_Expression`: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral) \ |
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`Sentiment_Target`: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value. |
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### Data Splits |
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No standard splits are provided |
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## Dataset Creation |
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### Curation Rationale |
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[More Information Needed] |
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### Source Data |
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#### Initial Data Collection and Normalization |
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[More Information Needed] |
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#### Who are the source language producers? |
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[More Information Needed] |
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### Annotations |
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#### Annotation process |
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[More Information Needed] |
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#### Who are the annotators? |
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[More Information Needed] |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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[More Information Needed] |
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### Licensing Information |
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Make sure to read and agree to the [license](http://oma-project.com/ArSenL/ArSenTD_Lev_Intro) |
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### Citation Information |
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``` |
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@article{baly2019arsentd, |
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title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets}, |
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author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir}, |
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journal={arXiv preprint arXiv:1906.01830}, |
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year={2019} |
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} |
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``` |
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### Contributions |
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Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. |
