基于机器学习算法的不同语言新闻文本分类

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-06-19 DOI:10.55195/jscai.1311380

Sidar Agduk, Emrah Aydemir, Ayfer Polat

{"title":"基于机器学习算法的不同语言新闻文本分类","authors":"Sidar Agduk, Emrah Aydemir, Ayfer Polat","doi":"10.55195/jscai.1311380","DOIUrl":null,"url":null,"abstract":"As a result of the developments in technology, the internet is accepted as one of the most important sources of information today. Although it is possible to access a large number of data in a short time thanks to the Internet, it is critical to analyze this data correctly. The need for text mining is increasing day by day by processing and analyzing the increasingly irregular text type data in the digital environment and classifying them in a meaningful way. In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories. The data set consisting of 4000 news texts was classified using 41 different machine learning algorithms in the Weka program. The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"16 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of News Texts from Different Languages with Machine Learning Algorithms\",\"authors\":\"Sidar Agduk, Emrah Aydemir, Ayfer Polat\",\"doi\":\"10.55195/jscai.1311380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a result of the developments in technology, the internet is accepted as one of the most important sources of information today. Although it is possible to access a large number of data in a short time thanks to the Internet, it is critical to analyze this data correctly. The need for text mining is increasing day by day by processing and analyzing the increasingly irregular text type data in the digital environment and classifying them in a meaningful way. In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories. The data set consisting of 4000 news texts was classified using 41 different machine learning algorithms in the Weka program. The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.\",\"PeriodicalId\":48494,\"journal\":{\"name\":\"Journal of Artificial Intelligence and Soft Computing Research\",\"volume\":\"16 1\",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Artificial Intelligence and Soft Computing Research\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.55195/jscai.1311380\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Artificial Intelligence and Soft Computing Research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.55195/jscai.1311380","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

摘要

由于技术的发展，互联网被认为是当今最重要的信息来源之一。虽然有了互联网，可以在短时间内访问大量数据，但正确分析这些数据至关重要。对数字环境中日益不规则的文本类型数据进行处理和分析，并对其进行有意义的分类，对文本挖掘的需求日益增加。在本研究中，从在线德语、西班牙语、英语和土耳其语新闻网站获得的新闻文本按照预先确定的世界、体育、经济和政治类别进行分离。由4000个新闻文本组成的数据集在Weka程序中使用41种不同的机器学习算法进行分类。朴素贝叶斯多项式和朴素贝叶斯多项式更新算法的分类成功率最高，德语新闻文本的分类成功率为93.5%，英语新闻文本的分类成功率为93.3%，西班牙语新闻文本的分类成功率为82.8%，土耳其语新闻文本的分类成功率为88.8%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Classification of News Texts from Different Languages with Machine Learning Algorithms

As a result of the developments in technology, the internet is accepted as one of the most important sources of information today. Although it is possible to access a large number of data in a short time thanks to the Internet, it is critical to analyze this data correctly. The need for text mining is increasing day by day by processing and analyzing the increasingly irregular text type data in the digital environment and classifying them in a meaningful way. In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories. The data set consisting of 4000 news texts was classified using 41 different machine learning algorithms in the Weka program. The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

7.00

自引率

25.00%

发文量

审稿时长

24 weeks

期刊介绍： Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.