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Emotion detection and semantic trends during COVID-19 social isolation using artificial intelligence techniques

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A Correction to this article was published on 01 February 2024

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Abstract

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends helps to implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence (AI) models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion-semantic analysis and trend detection. Regarding the significant applications of this research work, the experimental results revealed that our AI-based emotion-semantic aspects can effectively uncover people’s emotional reactions during a pandemic, especially when abiding to the stay-at-home preventive measure. Moreover, the research can be applied to uncover reactions to similar public health policies that affect people’s well-being.

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Acknowledgements

We acknowledge that already a preprint of this manuscript has been uploaded to arXiv (http://arxiv.org/abs/2101.06484), medrxiv (https://doi.org/10.1101/2021.01.16.21249943).

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Author notes

  1. Hamed Jelodar, Rita Orji, Stan Matween, Swarna Weerasinghe, Oladapo Oyebode and Yongli Wang contributed equally to this work.

Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, Canada

    Hamed Jelodar, Rita Orji, Stan Matween & Oladapo Oyebode

  2. Institute of Computer Science Polish Academy of Sciences, Warsaw, Poland

    Stan Matween

  3. Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada

    Swarna Weerasinghe

  4. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Hamed Jelodar & Yongli Wang

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  1. Hamed Jelodar
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  2. Rita Orji
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Correspondence to Hamed Jelodar.

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Jelodar, H., Orji, R., Matween, S. et al. Emotion detection and semantic trends during COVID-19 social isolation using artificial intelligence techniques. J Ambient Intell Human Comput 14, 16985–16993 (2023). https://doi.org/10.1007/s12652-023-04712-8

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  • DOI: https://doi.org/10.1007/s12652-023-04712-8

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