{"title":"泰国新闻文章分类模型的性能检验","authors":"Arisara Noppakaow, O. Uchida","doi":"10.1109/ICITEED.2019.8929959","DOIUrl":null,"url":null,"abstract":"This research aims to examine automatic models to classify Thai online news articles. The data set is six thousands of news articles from three mainstream websites. The news articles are classified into four categories—crime news, politic news, sport news, and entertainment news. Examinations on the classification algorithms of Decision Tree, Support Vector Machine (SVM), and Deep Learning are conducted. The performance is measured by the accuracy, the recall, the precision, and the F-Measure. The results show that the accuracies of Decision Tree, SVM, and Deep Learning models are 86%, 94%, and 95%, respectively.","PeriodicalId":6598,"journal":{"name":"2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"229 1","pages":"1-4"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Examinations on the Performance of Classification Models for Thai News Articles\",\"authors\":\"Arisara Noppakaow, O. Uchida\",\"doi\":\"10.1109/ICITEED.2019.8929959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research aims to examine automatic models to classify Thai online news articles. The data set is six thousands of news articles from three mainstream websites. The news articles are classified into four categories—crime news, politic news, sport news, and entertainment news. Examinations on the classification algorithms of Decision Tree, Support Vector Machine (SVM), and Deep Learning are conducted. The performance is measured by the accuracy, the recall, the precision, and the F-Measure. The results show that the accuracies of Decision Tree, SVM, and Deep Learning models are 86%, 94%, and 95%, respectively.\",\"PeriodicalId\":6598,\"journal\":{\"name\":\"2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE)\",\"volume\":\"229 1\",\"pages\":\"1-4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITEED.2019.8929959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITEED.2019.8929959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Examinations on the Performance of Classification Models for Thai News Articles
This research aims to examine automatic models to classify Thai online news articles. The data set is six thousands of news articles from three mainstream websites. The news articles are classified into four categories—crime news, politic news, sport news, and entertainment news. Examinations on the classification algorithms of Decision Tree, Support Vector Machine (SVM), and Deep Learning are conducted. The performance is measured by the accuracy, the recall, the precision, and the F-Measure. The results show that the accuracies of Decision Tree, SVM, and Deep Learning models are 86%, 94%, and 95%, respectively.
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