When COVID-19 lockdowns hit, food delivery platforms went from convenience to essential service almost overnight. This project traces how consumer emotion shifted through that transition by combining social media text, news coverage, and financial market data for food delivery platforms, using NLP, statistical, and time-series methods to connect emotional signal to stock performance.

The analysis centers on two emotions in particular — disgust and fear — tracking how their prevalence in public discourse moved with lockdown milestones, and whether those emotional swings show up as measurable effects on the platforms’ market valuations. It’s a case study in how crisis-driven consumer psychology propagates into financial markets in near real time.

Abstract

This paper examines consumer decision-making during crisis by analyzing social media, news, and financial market data from food delivery platforms that became essential services during COVID-19 community lockdowns. Using NLP, statistical, and time-series methods, the authors trace how consumer emotions — particularly disgust and fear — shifted over the course of the pandemic, and how those shifts related to the stock performance of food delivery platforms.