探索COVID-19对航空业的影响:一种文本挖掘方法

Swapna Gottipati, Kyong Jin Shim, Angeline Weiling Jiang, Andre Justin Sheng Wei Lee
{"title":"探索COVID-19对航空业的影响:一种文本挖掘方法","authors":"Swapna Gottipati, Kyong Jin Shim, Angeline Weiling Jiang, Andre Justin Sheng Wei Lee","doi":"10.1109/IEMCON51383.2020.9284858","DOIUrl":null,"url":null,"abstract":"Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"85 1","pages":"0208-0215"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Exploring the Impact of COVID-19 on Aviation Industry: A Text Mining Approach\",\"authors\":\"Swapna Gottipati, Kyong Jin Shim, Angeline Weiling Jiang, Andre Justin Sheng Wei Lee\",\"doi\":\"10.1109/IEMCON51383.2020.9284858\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic.\",\"PeriodicalId\":6871,\"journal\":{\"name\":\"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)\",\"volume\":\"85 1\",\"pages\":\"0208-0215\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMCON51383.2020.9284858\",\"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 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMCON51383.2020.9284858","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

我们的研究对2020年上半年FlightGlobal网站上的新闻文章进行了全面分析。我们的分析揭示了有关2019冠状病毒病期间航空业主题和趋势的有用见解。我们应用文本挖掘和自然语言处理技术对文章进行分析,以提取航空主题和文章情绪(积极和消极)。我们的研究结果表明,对与疫情现实发展相一致的主题的情绪趋势存在差异。文章情绪分析可以让行业参与者快速了解行业发展的本质。我们的文章主题分析通过总结积极和消极语料库中的共同关键主题,进一步增加了价值,使航空业的利益相关者能够更深入地了解受疫情影响的关注领域或方面。
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
Exploring the Impact of COVID-19 on Aviation Industry: A Text Mining Approach
Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article theme analysis adds further value by summarizing the common key topics within the positive and negative corpora, allowing stakeholders in the aviation industry to gain more insights on areas of concerns or aspects that are affected by the pandemic.
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
Financial Time Series Stock Price Prediction using Deep Learning Development of a Low-cost LoRa based SCADA system for Monitoring and Supervisory Control of Small Renewable Energy Generation Systems A Systematic Literature Review in Causal Association Rules Mining Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks Analysis of Requirements for Autonomous Driving Systems
×
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