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. 2021 Mar:144:110708.
doi: 10.1016/j.chaos.2021.110708. Epub 2021 Jan 22.

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

Affiliations

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

Vedika Gupta et al. Chaos Solitons Fractals. 2021 Mar.

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.

Keywords: COVID-19; Emotion; Multimodal Data; Natural Language Processing (NLP); Real-time Tweets; Textual Data.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Emotion-care scheme.
Fig. 2
Fig. 2
Word Cloud of tweeted words.
Fig. 3
Fig. 3
Web Application https://emotionofindia.herokuapp.com.
Fig. 4
Fig. 4
Indian State-wise Emotion Score: (a) Anger; (b) Anticipation; (c) Disgust; (d) Fear; (e) Joy; (f) Sadness; (g) Surprise; (h) Trust.
Fig. 5
Fig. 5
Multimodal Emotion Analysis: (a) Education; (b) Health; (c) Politics; (d) Lockdown; (e) Market; (f) Nature.
Fig. 6
Fig. 6
Emotion intensity bubble plots pertaining to six modes: (a) Anger; (b) Anticipation; (c) Disgust; (d) Fear; (e) Joy; (f) Surprise; (g) Trust; (h) Sadness.

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