A Novel Paradigm for Sentiment Analysis on COVID-19 Tweets with Transfer Learning Based Fine-Tuned BERT

Amit Pimpalkar; Jeberson Retna Raj

doi:10.46604/aiti.2023.11743

Authors

Amit Pimpalkar School of Computing, Sathyabama Institute of Science and Technology, Chennai, India; Department of Computer Science and Engineering (AIML), Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Jeberson Retna Raj School of Computing, Sathyabama Institute of Science and Technology, Chennai, India

DOI:

https://doi.org/10.46604/aiti.2023.11743

Keywords:

COVID-19, pre-trained, sentiment analysis, BERT, transfer learning

Abstract

The rapid escalation in global COVID-19 cases has engendered profound emotions of fear, agitation, and despondency within society. It is evident from COVID-19-related tweets that spark panic and elevate stress among individuals. Analyzing the sentiment expressed in online comments aids various stakeholders in monitoring the situation. This research aims to improve the performance of pre-trained bidirectional encoder representations from transformers (BERT) by employing transfer learning (TL) and fine hyper-parameter tuning (FT). The model is applied to three distinct COVID-19-related datasets, and each of the datasets belongs to a different class. The evaluation of the model’s performance involves six different machine learning (ML) classification models. This model is trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. Heat maps are generated for each model to visualize the results. The performance of the model demonstrates accuracies of 83%, 97%, and 98% for Class-5, Class-3, and binary classifications, respectively.

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