{"title":"机器学习技术在移动应用中仇恨语音检测的应用","authors":"Bujar Raufi, Ildi Xhaferri","doi":"10.1109/INFOTECH.2018.8510738","DOIUrl":null,"url":null,"abstract":"The proliferation of data through various platforms and applications is in constant increase. The versatility of data and its omnipresence makes it very hard to detect the trustworthiness and intention of the source. This is very evident in dynamic environments such as mobile applications. As a result, designing mobile applications that will monitor, control and block any type of malintents is important. This paper makes an attempt in this direction by implementing a lightweight machine learning classification scheme for hate speech detection in Albanian Language for mobile applications. Initial testing and evaluations indicate good classifier accuracy in mobile environments where frequent and real-time training of the algorithm is required.","PeriodicalId":142221,"journal":{"name":"2018 International Conference on Information Technologies (InfoTech)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Application of Machine Learning Techniques for Hate Speech Detection in Mobile Applications\",\"authors\":\"Bujar Raufi, Ildi Xhaferri\",\"doi\":\"10.1109/INFOTECH.2018.8510738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proliferation of data through various platforms and applications is in constant increase. The versatility of data and its omnipresence makes it very hard to detect the trustworthiness and intention of the source. This is very evident in dynamic environments such as mobile applications. As a result, designing mobile applications that will monitor, control and block any type of malintents is important. This paper makes an attempt in this direction by implementing a lightweight machine learning classification scheme for hate speech detection in Albanian Language for mobile applications. Initial testing and evaluations indicate good classifier accuracy in mobile environments where frequent and real-time training of the algorithm is required.\",\"PeriodicalId\":142221,\"journal\":{\"name\":\"2018 International Conference on Information Technologies (InfoTech)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Information Technologies (InfoTech)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFOTECH.2018.8510738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Information Technologies (InfoTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOTECH.2018.8510738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Machine Learning Techniques for Hate Speech Detection in Mobile Applications
The proliferation of data through various platforms and applications is in constant increase. The versatility of data and its omnipresence makes it very hard to detect the trustworthiness and intention of the source. This is very evident in dynamic environments such as mobile applications. As a result, designing mobile applications that will monitor, control and block any type of malintents is important. This paper makes an attempt in this direction by implementing a lightweight machine learning classification scheme for hate speech detection in Albanian Language for mobile applications. Initial testing and evaluations indicate good classifier accuracy in mobile environments where frequent and real-time training of the algorithm is required.