References
- Amazon Web Service Labs (AWSLabs). (2020). Amazon Sagemaker examples. Amazon Web Serice Labs. Retrieved May 3, 2020, from https://github.com/awslabs
- Badmus, M. O. (2020). When the storm is over: Sentiments, communities and information flow in the aftermath of Hurricane Dorian. International Journal of Disaster Risk Reduction, 47, 101645. https://doi.org/10.1016/j.ijdrr.2020.101645
- Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., & Chowell, G. (2020). A large-scale COVID-19 Twitter chatter dataset for open scientific research–an international collaboration. arXiv Preprint, arXiv:2004.03688.
- Barkur, G., & Vibha, G. B. K. (2020). Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian Journal of Psychiatry, 51, 102089. https://doi.org/10.1016/j.ajp.2020.102089
- BBC News. (April 3, 2020). Coronavirus: India’s bailout may not be enough to save economy. British Broadcasting Corporation (BBC). https://www.bbc.com/news/world-asia-india-52117704
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
- Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
- Chen, Q., Min, C., Zhang, W., Wang, G., Ma, X., & Evans, R. (2020). Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis. Computers in Human Behavior, 110, 106380. https://doi.org/10.1016/j.chb.2020.106380
- Chen, S., Mao, J., Li, G., Ma, C., & Cao, Y. (2020). Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective – A case study of Hurricane Harvey. Telematics and Informatics, 47, 101326. https://doi.org/10.1016/j.tele.2019.101326
- Das, S., Dixon, K., Sun, X., Dutta, A., & Zupancich, M. (2017). Trends in transportation research: Exploring content analysis in topics. Transportation Research Record: Journal of the Transportation Research Board, 2614(1), 27–38. https://doi.org/10.3141/2614-04
- Das, S., Dutta, A., & Brewer, M. (2020). Transportation research record articles: A case study of trend mining. In Transportation Research Record. (in press).
- Das, S., Sun, X., & Dutta, A. (2016). Text mining and topic modeling of compendiums of papers from transportation research board annual meetings. Transportation Research Record: Journal of the Transportation Research Board, 2552(1), 48–56. https://doi.org/10.3141%2F2552-07
- Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533–534. https://doi.org/10.1016/S1473-3099(20)30120-1
- Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3–4), 169–200. https://doi.org/10.1080/02699939208411068
- Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
- Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys (CSUR), 47(4), 1–38. https://doi.org/10.1145/2771588
- Jain, V. K., & Kumar, S. (2015). An effective approach to track levels of influenza-A (H1N1) pandemic in India using twitter. Procedia Computer Science, 70, 801–807. https://doi.org/10.1016/j.procs.2015.10.120
- Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., & Cebrian, M. (2016). Rapid assessment of disaster damage using social media activity. Science Advances, 2(3), e1500779. https://doi.org/10.1126/sciadv.1500779
- Li, Z., Wang, C., Emrich, C. T., & Guo, D. (2018). A novel approach to leveraging social media for rapid flood mapping: A case study of the 2015 South Carolina floods. Cartography and Geographic Information Science, 45(2), 97–110. https://doi.org/10.1080/15230406.2016.1271356
- Limaye, R. J., Sauer, M., Ali, J., Bernstein, J., Wahl, B., Barnhill, A., & Labrique, A. (2020). Building trust while influencing online COVID-19 content in the social media world. The Lancet Digital Health, 2(6), e277–e278. https://doi.org/10.1016/S2589-7500(20)30084-4
- Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
- Liu, B. F., & Kim, S. (2011). How organizations framed the 2009 H1N1 pandemic via social and traditional media: Implications for US health communicators. Public Relations Review, 37(3), 233–244. https://doi.org/10.1016/j.pubrev.2011.03.005
- Lu, Y., & Zhang, L. (2020). Social media WeChat infers the development trend of COVID-19. The Journal of Infection. 81, 82-83. https://doi.org/10.1016/j.jinf.2020.03.050
- Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
- Michalke, M., Brown, E., Mirisola, A., Brulet, A., & Hauser, L. (2018). koRpus: An R package for text analysis. The Comprehensive R Archive Network. Retrieved May 3, 2020, from https://cran.r-project.org/web/packages/koRpus/koRpus.pdf
- Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
- Neppalli, V. K., Caragea, C., Squicciarini, A., Tapia, A., & Stehle, S. (2017). Sentiment analysis during Hurricane Sandy in emergency response. International Journal of Disaster Risk Reduction, 21, 213–222. https://doi.org/10.1016/j.ijdrr.2016.12.011
- Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
- Plutchik, R. (1994). The psychology and biology of emotion. HarperCollins College Publishers.
- Press Information Bureau (PIB). (2020). PIB’s Special Webpage on COVID19. Government of India. https://pib.gov.in/newsite/coronaviruss.aspx
- Rajkumar, R. P. (2020). COVID-19 and mental health: A review of the existing literature. Asian Journal of Psychiatry, 52, 102066. https://doi.org/10.1016/j.ajp.2020.102066
- Rinker, T. W. (2016). sentimentr: Calculate text polarity sentiment. University at Buffalo/SUNY, Buffalo, New York. version 0.5, 3.
- Roy, D., Tripathy, S., Kar, S. K., Sharma, N., Verma, S. K., & Kaushal, V. (2020). Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID-19 pandemic. Asian Journal of Psychiatry, 51, 102083. https://doi.org/10.1016/j.ajp.2020.102083
- Sharma, M., Yadav, K., Yadav, N., & Ferdinand, K. C. (2017). Zika virus pandemic—analysis of Facebook as a social media health information platform. American Journal of Infection Control, 45(3), 301–302. https://doi.org/10.1016/j.ajic.2016.08.022
- Summers, E. (2017). DocNow Hydrator: GitHub repository. Github. https://github.com/DocNow/hydrator.
- Time Magazine (April 3, 2020). It was Already Dangerous to Be Muslim in India. Then Came the Coronavirus. Time Magazine. Retrieved May 3, 2020 from https://time.com/5815264/coronavirus-india-islamophobia-coronajihad/
- Wang, Z., Lam, N. S., Obradovich, N., & Ye, X. (2019). Are vulnerable communities digitally left behind in social responses to natural disasters? An evidence from Hurricane Sandy with Twitter data. Applied Geography, 108, 1–8. https://doi.org/10.1016/j.apgeog.2019.05.001
- Wang, Z., & Ye, X. (2018). Social media analytics for natural disaster management. International Journal of Geographical Information Science, 32(1), 49–72. https://doi.org/10.1080/13658816.2017.1367003
- World Health Organization (WHO). (January 12, 2020). Novel coronavirus – China. World Health Organization. https://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/