Abstract
Considering the COVID-19 outbreak, comprehending the psychological state is a major concern across the world. Sentiments and their emotions can be accessed via diverse social media platforms. Most prominently, Twitter plays a vital role in understanding the emotions of netizens, regardless of their origin. In this chapter, we study emotional health during the lockdown phases, taking India as a case study. Varied emotions over time derive their possible existence from the reported, deceased, and recovered cases, or a number of unanticipated situations. This study’s empirical findings are based upon eight emotions: Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust. We also describe how every lockdown impacted the emotions among people in worst hit Indian states towards COVID-19 cases by analyzing how a particular lockdown comes to be associated with distress and relief. For better understanding, we developed an automated tool to pictorially represent emotions, URL: https://emotiontrackerindia.herokuapp.com/. Understanding the emotional and mental health of the masses makes the nations proactive and future-ready. Adoption of suitable sustainability measures at the right time mitigates such crisis-like situations. This chapter puts forth an emotion analysis mechanism using social media and recommendations for upcoming emergencies.
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References
Chaudhary, A., Gupta, V., Jain, N., Santosh, K.: COVID-19 on air quality index (AQI): a necessary evil? In: COVID-19: Prediction, Decision-Making, and its Impacts. Springer, Singapore (2019)
Hou, Z., Du, F., Jiang, H., et al.: Assessment of public attention, risk perception, emotional and behavioural responses to the COVID-19 outbreak: social media surveillance in China. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3551338
Siegrist, M., Zingg, A.: The role of public trust during pandemics. Eur. Psychol. 19, 23–32 (2014). https://doi.org/10.1027/1016-9040/a000169
Jain, N., Jhunthra, S., Garg, H., Gupta, V., Mohan, S., Ahmadian, A., Salahshour, S., Ferrara, M.: Prediction modelling of COVID using machine learning methods from B-cell dataset. Results Phys. 21, 103813 (2021)
Byass, P.: Cause-specific mortality findings from the Global Burden of Disease project and the INDEPTH Network. Lancet Glob. Health 4, e785–e786 (2016)
Garriga, M., Agasi, I., Fedida, E., Pinzón‐Espinosa, J., Vazquez, M., Pacchiarotti, I., Vieta, E.: The role of mental health home hospitalization care during the COVID‐19 pandemic. Acta Psychiatr. Scand. 141, 479–480 (2020)
Rubin, G., Wessely, S.: The psychological effects of quarantining a city. BMJ m313 (2020)
Gupta, V., Jain, N., Katariya, P., Kumar, A., Mohan, S., Ahmadian, A., Ferrara, M.: An emotion care model using multimodal textual analysis on COVID-19. Chaos Solitons Fractals 144, 110708 (2021)
Gupta, V., Singh, V., Mukhija, P., Ghose, U.: Aspect-based sentiment analysis of mobile reviews. J. Intell. Fuzzy Syst. 36, 4721–4730 (2019)
Piryani, R., Gupta, V., Kumar Singh, V.: Generating aspect-based extractive opinion summary: drawing inferences from social media texts. Comput. Sist. 22 (2018)
Gupta, V., Singh, V., Ghose, U., Mukhija, P.: A quantitative and text-based characterization of big data research. J. Intell. Fuzzy Syst. 36, 4659–4675 (2019)
Roy, D., Tripathy, S., Kar, S., Sharma, N., Verma, S., Kaushal, V.: Study of knowledge, attitude, anxiety & perceived mental healthcare need in Indian population during COVID-19 pandemic. Asian J. Psychiatry 51, 102083 (2020)
Gupta, V., Gosain, A.: A comprehensive review of unstructured data management approaches in data warehouse. In: 2013 International Symposium on Computational and Business Intelligence, pp. 64–67. IEEE (2013)
D’Ambrogio, E.: India: The Biggest Democracy in the World. European Parliamentary Research Service, India (2014)
Mohammad, S., Turney, P.: NRC Emotion Lexicon, p. 2. National Research Council, Canada (2013)
Plutchik, R.: A psychoevolutionary theory of emotions. Soc. Sci. Inf. 21, 529–553 (1982)
Li, S., Wang, Y., Xue, J., et al.: The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users. Int. J. Environ. Res. Public Health 17, 2032 (2020). https://doi.org/10.3390/ijerph17062032
Chehal, D., Gupta, P., Gulati, P.: COVID-19 pandemic lockdown: an emotional health perspective of Indians on Twitter. Int. J. Soc. Psychiatry 002076402094074 (2020). https://doi.org/10.1177/0020764020940741
Aslam, F., Awan, T., Syed, J., et al.: Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanit. Soc. Sci. Commun. (2020). https://doi.org/10.1057/s41599-020-0523-3
Cao, W., Fang, Z., Hou, G., et al.: The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res. 287, 112934 (2020). https://doi.org/10.1016/j.psychres.2020.112934
Landicho-Pastor, C.: Sentiment analysis on synchronous online delivery of instruction due to extreme community quarantine in the Philippines caused by COVID-19 pandemic. Asian J. Multidiscip. Stud. 3(1), 1–6 (2020)
Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., Shah, Z.: Top concerns of tweeters during the COVID-19 pandemic: infoveillance study. J. Med. Internet Res. 22, e19016 (2020)
Heffner, J., Vives, M., FeldmanHall, O.: Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic. Pers. Individ. Differ. 170, 110420 (2021)
Galhardas, H., Florescu, D., Shasha, D., Simon, E., Saita, C.: Declarative data cleaning: language, model, and algorithms (2001)
Sethi, J., Mittal, M.: Monitoring the impact of air quality on the COVID-19 fatalities in Delhi, India: using machine learning techniques. Disaster Med. Public Health Prep. 1–8 (2020)
Sethi, J.K., Mittal, M.: A new feature selection method based on machine learning technique for air quality dataset. J. Stat. Manag. Syst. 22(4), 697–705 (2019)
Agrawal, R., Gupta, N.: Analysis of COVID-19 data using machine learning techniques. Data Anal. Manage. 53, 595–603 (2021)
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Dhingra, S., Arora, R., Katariya, P., Kumar, A., Gupta, V., Jain, N. (2021). Understanding Emotional Health Sustainability Amidst COVID-19 Imposed Lockdown. In: Agrawal, R., Mittal, M., Goyal, L.M. (eds) Sustainability Measures for COVID-19 Pandemic. Springer, Singapore. https://doi.org/10.1007/978-981-16-3227-3_12
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