Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach

The Covid-19 pandemic has significantly reshaped societies, prompting an unprecedented surge in digital interactions and communication via social media platforms. Amidst this evolving landscape, the analysis of public sentiment towards pandemic management strategies has emerged as a critical avenue for understanding societal responses. This paper delves into the intricate landscape of sentiment dynamics surrounding the Covid-19 pandemic, with a focus on sentiments expressed in Twitter posts. Leveraging sentiment analysis techniques, the study provides nuanced insights into societal attitudes, concerns, and perceptions across distinct phases of the pandemic. The investigation not only employs traditional lexicon-based sentiment analysis but also explores the intersection of sentiment analysis and machine learning algorithms. The research contributes to the discourse by presenting a comprehensive analysis of public sentiment dynamics during various pandemic stages, shedding light on evolving emotional responses and offering insights into the effectiveness of measures.