使用“现成”词典衡量新闻情绪的四个最佳实践:大规模p-hacking实验

Chung-hong Chan, Joseph W. Bajjalieh, L. Auvil, Hartmut Wessler, Scott L. Althaus, Kasper Welbers, Wouter van Atteveldt, Marc Jungblut
{"title":"使用“现成”词典衡量新闻情绪的四个最佳实践:大规模p-hacking实验","authors":"Chung-hong Chan, Joseph W. Bajjalieh, L. Auvil, Hartmut Wessler, Scott L. Althaus, Kasper Welbers, Wouter van Atteveldt, Marc Jungblut","doi":"10.31235/osf.io/np5wa","DOIUrl":null,"url":null,"abstract":"We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated the risk of generating a spectrum of results with different levels of statistical significance by presenting an analysis of relationships between news sentiment and U.S. presidential approval. We summarize our findings into four best practices: 1) use a suitable sentiment dictionary; 2) do not assume that the validity and reliability of the dictionary is ‘built-in’; 3) check for the influence of content length and 4) do not use multiple dictionaries to test the same statistical hypothesis.","PeriodicalId":275035,"journal":{"name":"Computational Communication Research","volume":"14 36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Four best practices for measuring news sentiment using ‘off-the-shelf’ dictionaries: a large-scale p-hacking experiment\",\"authors\":\"Chung-hong Chan, Joseph W. Bajjalieh, L. Auvil, Hartmut Wessler, Scott L. Althaus, Kasper Welbers, Wouter van Atteveldt, Marc Jungblut\",\"doi\":\"10.31235/osf.io/np5wa\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated the risk of generating a spectrum of results with different levels of statistical significance by presenting an analysis of relationships between news sentiment and U.S. presidential approval. We summarize our findings into four best practices: 1) use a suitable sentiment dictionary; 2) do not assume that the validity and reliability of the dictionary is ‘built-in’; 3) check for the influence of content length and 4) do not use multiple dictionaries to test the same statistical hypothesis.\",\"PeriodicalId\":275035,\"journal\":{\"name\":\"Computational Communication Research\",\"volume\":\"14 36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Communication Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31235/osf.io/np5wa\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Communication Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31235/osf.io/np5wa","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

摘要

我们使用一个大型新闻语料库,基于基于词典的方法检验了37种情绪得分的有效性,并通过分析新闻情绪与美国总统支持率之间的关系,展示了产生具有不同统计显著性水平的结果谱的风险。我们将研究结果总结为四个最佳实践:1)使用合适的情感词典;2)不要认为字典的有效性和可靠性是“内置的”;3)检查内容长度的影响,4)不要使用多个字典来检验相同的统计假设。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Four best practices for measuring news sentiment using ‘off-the-shelf’ dictionaries: a large-scale p-hacking experiment
We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated the risk of generating a spectrum of results with different levels of statistical significance by presenting an analysis of relationships between news sentiment and U.S. presidential approval. We summarize our findings into four best practices: 1) use a suitable sentiment dictionary; 2) do not assume that the validity and reliability of the dictionary is ‘built-in’; 3) check for the influence of content length and 4) do not use multiple dictionaries to test the same statistical hypothesis.
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
自引率
0.00%
发文量
0
期刊最新文献
Using State-of-the-art Emotion Detection Models in a Crisis Communication Context How COVID-19 and the News Shaped Populism in Facebook Comments in Seven European Countries. : A Computational Analysis. Agent-based modeling of diversity, new information and minority groups in opinion formation Going Micro to Go Negative? Algorithmic Recommendations’ Role for the Interrelatedness of Counter-Messages and Polluted Content on YouTube – A Network Analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1