Comparison between sentiments of people from affected and non-affected regions after the flood

References

• Blei DM, Ng AY, Jordan MI. 2003. Latent dirichlet allocation. J Mach Learn Res. 3:993–1022.
   Web of Science ®Google Scholar
• Butt TA. 2019. Context‐aware cognitive disaster management using fog‐based internet of things. Trans Emerging Tel Tech. e3646.
   Web of Science ®Google Scholar
• Carroll B, Morbey H, Balogh R, Araoz G. 2009. Flooded homes, broken bonds, the meaning of home, psychological processes and their impact on psychological health in a disaster. Health Place. 15(2):540–547.
   PubMed Web of Science ®Google Scholar
• Cheng X. 2020. Flood risk and flood management policies in China. In: Annual Report on China's Response to Climate Change; 2017; Singapore: Springer. p. 49–65.
   Google Scholar
• China Meterological Administration. 2021. China climate bulletin 2020. China Meterological Administration; [accessed 2021 Feb 9]. http://www.cma.gov.cn/2011xwzx/2011xqxxw/2011xqxyw/202102/t20210209_571871.html.
   Google Scholar
• Cohn R, Carley KM, Harrald JR, Wallace WA. 2000. Emotions in crisis management: an analysis of the organisational response of two natural disasters. IJTM. 19(3/4/5):313–335.
   Google Scholar
• Eichstaedt JC, Smith RJ, Merchant RM, Ungar LH, Crutchley P, Preoţiuc-Pietro D, Asch DA, Schwartz HA. 2018. Facebook language predicts depression in medical records. Proc Natl Acad Sci U S A. 115(44):11203–11208.
   PubMed Web of Science ®Google Scholar
• Ferner C, Havas C, Birnbacher E, Wegenkittl S, Resch B. 2020. Automated seeded latent Dirichlet allocation for social media based event detection and mapping. Inform. 11(8):376.
   Google Scholar
• Ferrara E, Yang Z. 2015. Measuring emotional contagion in social media. PloS One. 10(11):e0142390.
   PubMed Web of Science ®Google Scholar
• Hassan SZ, Ahmad K, Al-Fuqaha A, Conci N. 2019. Sentiment analysis from images of natural disasters. In: International conference on image analysis and processing. Cham: Springer; p. 104–113.
   Google Scholar
• Huang R, Sun X. 2014. Weibo network, information diffusion and implications for collective action in China. Inform Commun Soc. 17(1):86–104.
   Web of Science ®Google Scholar
• Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L. 2019. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 78(11):15169–15211.
   Web of Science ®Google Scholar
• Karmegam D, Mappillairaju B. 2020. Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis. Int J Health Geog. 19:1–13.
   PubMed Web of Science ®Google Scholar
• Lan YX. 2013. Research on the model of public opinion diffusion law of emergency Weibo. Inform Sci. 3:31–34.
   Google Scholar
• Liu ZM, Liu L. 2012. An empirical study on emotion classification of Chinese Weibo based on machine learning. Comput Eng Appl. 48 (1):1–4.
   Google Scholar
• Ministry of Emergency Management of the People's Republic of China. 2021. The Ministry of Emergency Management announced the top 10 natural disasters in China for 2020. Ministry of Emergency Management of the people's Republic of China; [accessed 2021 Jan 2]. https://www.mem.gov.cn/xw/yjglbgzdt/202101/t20210102_376288.shtml.
   Google Scholar
• Murthy D, Longwell SA. 2013. Twitter and disasters: the uses of twitter during the 2010 Pakistan floods. Inf Commun Soc. 16(6):837–855.
   Web of Science ®Google Scholar
• Ogie RI, Verstaevel N. 2020. Disaster informatics: an overview. Prog Disaster Sci. 7:100111.
   Google Scholar
• Pang A. 2010. Future directions of crisis communication research: emotions in crisis–the next frontier. [accessed 2010 Feb 1]. https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7036&context=lkcsb_research.
   Google Scholar
• People's Daily. 2020. Do a good job in flood control in the south and strengthen flood preparation in the north. [accessed 2020 Jul 14]. http://society.people.com.cn/n1/2020/0714/c1008-31781835.html.
   Google Scholar
• Said N, Ahmad K, Riegler M, Pogorelov K, Hassan L, Ahmad N, Conci N. 2019. Natural disasters detection in social media and satellite imagery: a survey. Multimed Tools Appl. 78(22):31267–31302.
   Web of Science ®Google Scholar
• Scheffe H. 1999. The analysis of variance. Vol. 72. New York:John Wiley & Sons.
   Google Scholar
• Schipper M, Petermann F. 2013. Relating empathy and emotion regulation: do deficits in empathy trigger emotion dysregulation? Soc Neurosci. 8(1):101–107.
   PubMed Web of Science ®Google Scholar
• Scott SB, Sliwinski MJ, Zawadzki M, Stawski RS, Kim J, Marcusson-Clavertz D, Lanza ST, Conroy DE, Buxton O, Almeida DM, et al. 2020. A coordinated analysis of variance in affect in daily life. Assessment. 27(8):1683–1698.
   PubMed Web of Science ®Google Scholar
• Shou HR, Deng SQ, Xu J. 2017. Internet rumor recognition method based on emotion analysis. Data Anal Knowl Disc. 1(07):44–51.
   Google Scholar
• Stieglitz S, Dang-Xuan L. 2013. Emotions and information diffusion in social media – sentiment of microblogs and sharing behavior. J Manage Inform Syst. 29(4):217–248.
   Web of Science ®Google Scholar
• Sun W, Zhang XL. 2011. The expression and guidance of network public opinion in sudden natural disasters: taking the earthquake and tsunami in eastern Japan as an example. Acad Explor. 06:115–118.
   Google Scholar
• Tukey JW. 1949. Comparing individual means in the analysis of variance. Biometrics. 5(2):99–114.
   PubMed Web of Science ®Google Scholar
• United Nations Office for Disaster Risk Reduction. 2019. The cost of disasters to humanity: an overview of the past 20 years (2000–2019). United Nations Office for Disaster Risk Reduction; [accessed 2020 Oct 12]. https://www.under.org/news/drrday-un-report-charts-huge-rise-climate-disasters.
   Google Scholar
• Wang J, Zhou Y, Zhang W, Evans R, Zhu C. 2020. Concerns expressed by Chinese social media users during the COVID-19 pandemic: content analysis of Sina Weibo microblogging data. J Med Internet Res. 22(11):e22152.
   PubMed Web of Science ®Google Scholar
• Wang Y, Wu D, Yuan X. 2020. LDA-based deep transfer learning for fault diagnosis in industrial chemical processes. Comput Chem Eng. 140:106964.
   Web of Science ®Google Scholar
• Xie Q, Zhang X, Ding Y, Song M. 2020. Monolingual and multilingual topic analysis using LDA and BERT embeddings. J Informetr. 14(3):101055.
   Web of Science ®Google Scholar
• Xie XF, Wang M, Zhang RG, Li J, Yu QY. 2011. The role of emotions in risk communication. Risk Anal. 31(3):450–465.
   PubMed Web of Science ®Google Scholar
• Xu G, Yu Z, Yao H, Li F, Meng Y, Wu X. 2019. Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access. 7:43749–43762.
   Web of Science ®Google Scholar
• Xu LH, Lin HF, Zhao J. 2008. Construction and analysis of affective corpus. Chin Inform J. 01:116–122.9.
   Google Scholar
• Xue J, Chen J, Chen C, Zheng C, Li S, Zhu T. 2020. Public discourse and sentiment during the COVID 19 pandemic: using latent Dirichlet allocation for topic modeling on Twitter. PloS One. 15(9):e0239441.
   PubMed Web of Science ®Google Scholar
• Xue J, Chen J, Hu R, Chen C, Zheng C, Su Y, Zhu T. 2020. Twitter discussions and emotions about the COVID-19 pandemic: machine learning approach. J Med Internet Res. 22(11):e20550.
   PubMed Web of Science ®Google Scholar
• Yadollahi A, Shahraki AG, Zaiane OR. 2017. Current state of text sentiment analysis from opinion to emotion mining. ACM Comput Surv. 50(2):1–33.
   Web of Science ®Google Scholar
• Yang Y, Hsu JH, Löfgren K, Cho W. 2021. Cross-platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining. Soc Netw Anal Min. 11(1):1–18.
   Web of Science ®Google Scholar
• Yusoff S, Yusoff NH. 2021. Building social resilience after the 2014 flood disaster. Pertanika J Soc Sci Hum. 29(3):1–14
   Web of Science ®Google Scholar
• Zhang S, Wei Z, Wang Y, Liao T. 2018. Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Gener Comp Syst. 81:395–403.
   Web of Science ®Google Scholar
• Zhou X, Song Y, Jiang H, Wang Q, Qu Z, Zhou X, Jit M, Hou Z, Lin L. 2021. Comparison of public responses to containment measures during the initial outbreak and resurgence of COVID-19 in China: infodemiology study. J Med Internet Res. 23(4):e26518.
   PubMed Web of Science ®Google Scholar
• Zhou Y, Tian XL, Zhong MH. 2019. Public opinion analysis of Notre Dame fire based on Weibo Crawler. J Wuhan Univ Technol. 41(05):461–466.
   Google Scholar
• Zhu Y, Li X, Wang J. 2021. Analysis and research of Weibo public opinion based on text. 5th International Conference on Computer Science and Information Engineering; Vol. 1769; No. (1); p. 012018.
   Google Scholar