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dc.contributor.authorAlharbi, Basma
dc.contributor.authorAlamro, Hind
dc.contributor.authorAlshehri, Manal
dc.contributor.authorKhayyat, Zuhair
dc.contributor.authorKalkatawi, Manal
dc.contributor.authorJaber, Inji Ibrahim
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2020-11-09T12:56:27Z
dc.date.available2020-11-09T12:56:27Z
dc.date.issued2020-11-01
dc.identifier.urihttp://hdl.handle.net/10754/665873
dc.description.abstractThis paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively. Compared to other publicly released Arabic datasets, ASAD is a large, high-quality annotated dataset(including 95K tweets), with three-class sentiment labels (positive, negative and neutral). We presents the details of the data collection process and annotation process. In addition, we implement several baseline models for the competition task and report the results as a reference for the participants to the competition.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2011.00578
dc.rightsArchived with thanks to arXiv
dc.titleASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
dc.typePreprint
dc.contributor.departmentBusiness Operations
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.eprint.versionPre-print
dc.contributor.institutionUniv. of Jeddah , Saudi Arabia.
dc.contributor.institutionLucidya , Saudi Arabia.
dc.contributor.institutionKAU , Saudi Arabia.
dc.identifier.arxivid2011.00578
kaust.personAlamro, Hind
kaust.personAlshehri, Manal
kaust.personJaber, Inji Ibrahim
kaust.personZhang, Xiangliang
refterms.dateFOA2020-11-09T12:58:44Z


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