Sentiment analysis of Twitter data using machine learning: COVID-19 perspective

Shobhit Srivastava, Mrinal Kanti Sarkar and Chinmay Chakraborty

International Journal of Data Analysis Techniques and Strategies, 2024, vol. 16, issue 1, 1-16

Abstract: The 2019 COVID-19 pandemic has affected people worldwide. Social media has become a global platform for individuals to voice their diverse perspectives on the pandemic, which has significantly altered their lives during and beyond lockdown periods. Twitter, a leading social media platform, experienced a surge in coronavirus-related tweets encompassing a spectrum of positive, negative and neutral opinions. Coronavirus transmits between humans in numerous ways. It irritates the lungs. This makes Twitter a perfect platform for expressing opinions. Twitter data from across the world was collected and analysed for sentiment in order to better understand public opinion and prepare for COVID-19 (Tusar et al., 2022). In this article, our aim is to compare the neural network techniques and indicate the share of their performance measures. We use kNN and neural network algorithms for these and use the MSE factor as a key of comparison. However, we use other performance measures too for better analysis of the result. Our main focus in this study is to analyse the performance partition of the kNN algorithms, including the performance portion of the each algorithm.

Keywords: COVID-19; social media; sentiment analysis; Twitter; machine learning; neural networks; KNN; neural network. (search for similar items in EconPapers)
Date: 2024
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