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An Emotion Care Model using Multimodal Textual Analysis on COVID-19

Author

Listed:
  • Gupta, Vedika
  • Jain, Nikita
  • Katariya, Piyush
  • Kumar, Adarsh
  • Mohan, Senthilkumar
  • Ahmadian, Ali
  • Ferrara, Massimiliano

Abstract

At the dawn of the year 2020, the world was hit by a significant pandemic COVID-19, that traumatized the entire planet. The infectious spread grew in leaps and bounds and forced the policymakers and governments to move towards lockdown. The lockdown further compelled people to stay under house arrest, which further resulted in an outbreak of emotions on social media platforms. Perceiving people's emotional state during these times becomes critically and strategically important for the government and the policymakers. In this regard, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19. Moreover, this paper studies 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics. This is the first of its kind linguistic analysis on multiple modes pertaining to the pandemic to the best of our understanding. Taking India as a case study, we inferred from this textual analysis that ‘joy’ has been lesser towards everything (~9-15%) but nature (~17%) due to the apparent fact of lessened pollution. The education system entailed more trust (~29%) due to teachers' fraternity's consistent efforts. The health sector witnessed sadness (~16%) and fear (~18%) as the dominant emotions among the masses as human lives were at stake. Additionally, the state-wise and emotion-wise depiction is also provided. An interactive internet application has also been developed for the same.

Suggested Citation

  • Gupta, Vedika & Jain, Nikita & Katariya, Piyush & Kumar, Adarsh & Mohan, Senthilkumar & Ahmadian, Ali & Ferrara, Massimiliano, 2021. "An Emotion Care Model using Multimodal Textual Analysis on COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000618
    DOI: 10.1016/j.chaos.2021.110708
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    References listed on IDEAS

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    1. Ali, Khalid K. & Cattani, Carlo & Gómez-Aguilar, J.F. & Baleanu, Dumitru & Osman, M.S., 2020. "Analytical and numerical study of the DNA dynamics arising in oscillator-chain of Peyrard-Bishop model," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    3. Faheem Aslam & Tahir Mumtaz Awan & Jabir Hussain Syed & Aisha Kashif & Mahwish Parveen, 2020. "Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-9, December.
    4. Altan, Aytaç & Karasu, Seçkin, 2020. "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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    1. Arshad, Sadia & Siddique, Imran & Nawaz, Fariha & Shaheen, Aqila & Khurshid, Hina, 2023. "Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Ali, Javaid & Raza, Ali & Ahmed, Nauman & Ahmadian, Ali & Rafiq, Muhammad & Ferrara, Massimiliano, 2021. "Evolutionary optimized Padé approximation scheme for analysis of covid-19 model with crowding effect," Operations Research Perspectives, Elsevier, vol. 8(C).

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