Temporal Shifts in Sentiment: Analyzing Reddi...

Temporal Shifts in Sentiment: Analyzing Reddit Discourse Before, During and After COVID-19 Pandemic

Author Name : B.Akash, K.Dinesh, K.Dheepack, G.Hemanth, D.Ramesh

ABSTRACT :This paper employs a range of Natural Language Processing (NLP) techniques, including LSTM, RNN, CNN, and the VADER Sentiment Intensity Analyzer, to analyze Reddit discourse before, during, and after the COVID-19 pandemic. Despite utilizing diverse methods, the sentiment intensity analyzer from VADER emerged as the most effective approach. By collecting and analyzing data from relevant subreddits, temporal shifts in sentiment are quantified, revealing evolving public attitudes and perceptions. Through visualizations such as line charts and bar graphs, key sentiment trends are illustrated, shedding light on societal responses to the pandemic. The findings provide valuable insights into how NLP can be utilized to understand online discourse dynamics during crises, informing decision-making processes in public health, policy-making, and community engagement. This study underscores the importance of NLP in capturing and analyzing nuanced shifts in public sentiment across diverse online platforms.