{"title":"Author Gender Identification from Arabic Youtube Comments","authors":"Jihad Zahir, Youssef Mehdi Oukaja, H. Mousannif","doi":"10.1109/SITIS.2019.00109","DOIUrl":null,"url":null,"abstract":"In this paper, we present a machine learning based approach to gender identification from Arabic text in Youtube comments. The proposed methodology handles dialectal Arabic, addresses the limitations of state-of-the art approaches and achieves an accuracy of 92% and an average precision of 98%.","PeriodicalId":301876,"journal":{"name":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2019.00109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we present a machine learning based approach to gender identification from Arabic text in Youtube comments. The proposed methodology handles dialectal Arabic, addresses the limitations of state-of-the art approaches and achieves an accuracy of 92% and an average precision of 98%.