Sentiment Analysis of Pandemic Tweets with COVID-19 as a Prototype

One of the most important applications of text mining is sentiment analysis of pandemic tweets. For example, it can make governments able to predict the onset of pandemics and to put in place safe policies based on people's feelings. Many research studies addressed this issue using various datasets and models. Nevertheless, this is still an open area of research in which many datasets and models are yet to be explored. This paper is interested in the sentiment analysis of COVID-19 tweets as a prototype. Our literature review revealed that as the dataset size increases, the accuracy generally tends to decrease. This suggests that using a small dataset might provide misleading results that cannot be generalized. Hence, it is better to consider large datasets and try to improve analysis performance on it. Accordingly, in this paper we consider a huge dataset, namely COVIDSenti, which is composed of three sub datasets (COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C). These datasets have been processed with a number of Machine Learning (ML) models, Deep Learning (DL) models, and transformers. In this paper, we examine other ML and DL models aiming to find superior solutions. Specifically, we consider Ridge Classifier (RC), Multinomial Naïve Bayes (MNB), Stochastic Gradient Descent (SGD), Support Vector Classification (SVC), Extreme Gradient Boosting (XGBoost), and the DL Gated Recurrent Unit (GRU). Experimental results have shown that unlike the models that we tested, and the state-of-the-art models on the same dataset, SGD technique with count vectorizer showed quite constantly high performance on all the four datasets.

@article{Almutiri2024,
  title     = {Sentiment Analysis of Pandemic Tweets with COVID-19 as a Prototype},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mashail Almutiri and Mona Alghamdi and Hanan Elazhary},
  doi       = {10.14569/IJACSA.2024.0150453},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150453}
}