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Imbalanced COVID-19 vaccine sentiment classification with synthetic resampling coupled deep adversarial active learning

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Abstract

Despite an abundance of scientific evidence supporting the effectiveness of COVID-19 vaccines, there has been a recent global surge in vaccine hesitancy, primarily driven by the spread of misinformation on social media platforms. It is crucial to address this issue and raise awareness about the importance of vaccination in combating the deadly COVID-19 virus. Predicting community sentiment through social media platforms can provide valuable insights into vaccine hesitancy, aiding health workers and medical professionals in taking necessary precautionary measures. However, the lack of high-quality labeled data presents a challenge for building an effective COVID-19 sentiment classifier. Additionally, the available labeled datasets suffer from severe class imbalance. To address these challenges, this article presents an effective COVID-19 sentiment prediction framework. Firstly, a deep adversarial active learning framework leverages abundant unlabeled data by training autoencoder and discriminator components adversarially to select the most informative unlabeled samples. Secondly, to mitigate the effects of imbalanced labeled datasets, a resampling phase is incorporated into the adversarial training loop. The proposed framework, named Resampling Supported Deep Adversarial Active Learning (RS-DAAL), is rigorously evaluated using two different datasets comprising social media posts from Twitter and Reddit. Various resampling techniques, including undersampling, oversampling, and hybrid methods, are assessed, with oversampling techniques further tested at different levels of resampling. Comparative studies are conducted against a baseline model without any resampling layer and with current state-of-the-art methods as well. Experimental results and statistical analysis demonstrate the superiority of the proposed RS-DAAL method in identifying COVID-19 sentiments on social media platforms.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

    Sankhadeep Chatterjee & Asit Kumar Das

  2. Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India

    Saranya Bhattacharjee

  3. Narula Institute of Technology, Agarpara, Kolkata, West Bengal, 700109, India

    Soumen Banerjee

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Conceptualization: SC, Methodology: SC, Formal analysis and investigation: SB, SC, Writing: SB, SC, Writing—review and editing: SC, SB, AKD, Supervision: AKD, SB

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Original Data is available at ‘https://www.kaggle.com/datasets/gpreda/all-covid19-vaccines-tweets’. Annotated data will be made available upon request to the Corresponding Author.

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Chatterjee, S., Bhattacharjee, S., Das, A.K. et al. Imbalanced COVID-19 vaccine sentiment classification with synthetic resampling coupled deep adversarial active learning. Mach Learn 113, 8027–8059 (2024). https://doi.org/10.1007/s10994-024-06562-7

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