{"title":"泰国标题党新闻分类及其特点","authors":"Natnicha Wongsap, Tastanya Prapphan, Lisha Lou, S. Kongyoung, Sasiwimol Jumun, Natsuda Kaothanthong","doi":"10.1109/ICESIT-ICICTES.2018.8442064","DOIUrl":null,"url":null,"abstract":"Clickbait is a widely used writing style of the news headline that has been utilized to gain attentions from the reader for the revenues generated from the clicks. After the readers open the link to read the content of the clickbait headline, it leaves them a disappointment. In this work, the characteristic of Thai clickbait headlines is studied. To compare the effect of the special characters such as ‘!’, ‘?’, and ‘#’ on the classification, two datasets, which contains the special characters and the one without them, are provided. They have been utilized in the experiments using well-known classifiers such as Decision Tree, Support Vector Machine, and Naive Bayes. The result shows that the special characters and the decision tree classifier gives 99. 90% accuracy.","PeriodicalId":57136,"journal":{"name":"单片机与嵌入式系统应用","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Thai Clickbait Headline News Classification and its Characteristic\",\"authors\":\"Natnicha Wongsap, Tastanya Prapphan, Lisha Lou, S. Kongyoung, Sasiwimol Jumun, Natsuda Kaothanthong\",\"doi\":\"10.1109/ICESIT-ICICTES.2018.8442064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clickbait is a widely used writing style of the news headline that has been utilized to gain attentions from the reader for the revenues generated from the clicks. After the readers open the link to read the content of the clickbait headline, it leaves them a disappointment. In this work, the characteristic of Thai clickbait headlines is studied. To compare the effect of the special characters such as ‘!’, ‘?’, and ‘#’ on the classification, two datasets, which contains the special characters and the one without them, are provided. They have been utilized in the experiments using well-known classifiers such as Decision Tree, Support Vector Machine, and Naive Bayes. The result shows that the special characters and the decision tree classifier gives 99. 90% accuracy.\",\"PeriodicalId\":57136,\"journal\":{\"name\":\"单片机与嵌入式系统应用\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"单片机与嵌入式系统应用\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICESIT-ICICTES.2018.8442064\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"单片机与嵌入式系统应用","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICESIT-ICICTES.2018.8442064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Thai Clickbait Headline News Classification and its Characteristic
Clickbait is a widely used writing style of the news headline that has been utilized to gain attentions from the reader for the revenues generated from the clicks. After the readers open the link to read the content of the clickbait headline, it leaves them a disappointment. In this work, the characteristic of Thai clickbait headlines is studied. To compare the effect of the special characters such as ‘!’, ‘?’, and ‘#’ on the classification, two datasets, which contains the special characters and the one without them, are provided. They have been utilized in the experiments using well-known classifiers such as Decision Tree, Support Vector Machine, and Naive Bayes. The result shows that the special characters and the decision tree classifier gives 99. 90% accuracy.