{"title":"气候变化新闻的主题分析","authors":"S. Chawathe","doi":"10.1109/CCWC47524.2020.9031122","DOIUrl":null,"url":null,"abstract":"This paper explores the application of computational methods to the analysis of the large and growing corpus of news articles and related data on climate change. Topics are analyzed using Latent Dirichlet Allocation and methods customized to specific news sources that take advantage of keywords and other metadata that may be present. Results of this method on news articles drawn over several months are presented.","PeriodicalId":161209,"journal":{"name":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Topic Analysis of Climate-Change News\",\"authors\":\"S. Chawathe\",\"doi\":\"10.1109/CCWC47524.2020.9031122\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores the application of computational methods to the analysis of the large and growing corpus of news articles and related data on climate change. Topics are analyzed using Latent Dirichlet Allocation and methods customized to specific news sources that take advantage of keywords and other metadata that may be present. Results of this method on news articles drawn over several months are presented.\",\"PeriodicalId\":161209,\"journal\":{\"name\":\"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)\",\"volume\":\"166 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCWC47524.2020.9031122\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th Annual Computing and Communication Workshop and Conference (CCWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCWC47524.2020.9031122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper explores the application of computational methods to the analysis of the large and growing corpus of news articles and related data on climate change. Topics are analyzed using Latent Dirichlet Allocation and methods customized to specific news sources that take advantage of keywords and other metadata that may be present. Results of this method on news articles drawn over several months are presented.
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