ArticleChaos, solitons, and fractals2021
An Emotion Care Model using Multimodal Textual Analysis on COVID-19.
Article in Chaos, solitons, and fractals, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets.Scientific reports · 2024Article
- Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.JMIR formative research · 2024Article
- Mathematical modelling, analysis and numerical simulation of social media addiction and depression.PloS one · 2024Article
- Crisis communication in the WHO COVID-19 press conferences: A retrospective analysis.PloS one · 2023Article
- Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission.Physica A · 2023Article
- Short term safety of coronavirus disease 2019 vaccines in patients with solid tumors receiving systemic therapy.World journal of clinical oncology · 2022Article
- Opinions on Homeopathy for COVID-19 on Twitter.Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference · 2022Article
- Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries.Pattern recognition letters · 2022Article
- Improved COVID-19 detection with chest x-ray images using deep learning.Multimedia tools and applications · 2022Article
- Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.Frontiers in oncology · 2022Article
- An artificial intelligence method for comprehensive evaluation of preschool education quality.Frontiers in psychology · 2022Article
- Emotion research on education public opinion based on text analysis and deep learning.Frontiers in psychology · 2022Article
- Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to COVID-19: topic modelling and sentiment analysis.Journal of big data · 2022Article
- A Secure and reliable RFID authentication protocol using digital schnorr cryptosystem for IoT-enabled healthcare in COVID-19 scenario.Sustainable cities and society · 2021Article
- COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness.Safety science · 2021Article
- Emotional states and coping methods in nursing and non-nursing students responding to COVID-19: a cross-sectional study in China.BMJ open · 2021Article
- Analytical and qualitative investigation of COVID-19 mathematical model under fractional differential operator.Mathematical methods in the applied sciences · 2021Article
- Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio.Computer communications · 2021Article
- Improving the performance of deep learning models using statistical features: The case study of COVID-19 forecasting.Mathematical methods in the applied sciences · 2021Article
- The forecast of COVID-19 spread risk at the county level.Journal of big data · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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