One of the many sectors that has been highly affected by COVID-19 is retail industry. Fearful of contracting the virus, people are seen to avoid traditional bricks and mortar trades, which led to rapid diffusion of various online businesses. People are found to be more comfortable in online shopping than going to shopping malls physically during the pandemic. These sudden changes in shopping behavior have had crucial implications on business operations. It is important to understand how retail industry is transforming due to the pandemic for suggesting sustainable planning interventions for reconfiguration of urban core. This research conducts an exploratory analysis using public discourse data in Twitter to understand how retail is changing in the time of COVID-19 and e-commerce. First, using Twitter API, tweets related to COVID-19 crisis, in-store shopping, e-shopping and retails are extracted. Then, text mining and topic modelling is applied on the tweets to analyze general people’s real-time concerns, opinions and sentiments related to shopping in the time of pandemic. Multiple analytical frameworks are used, such as, wordclouds, sentiment analysis to identify frequently occurring words and topics in those discussions. This research also identifies challenges as well as solutions for safe reopening of conventional retails through analyzing general public opinions. Results of this study will assist policy makers to reshape retail structure with an aim to help revive economy in the post-COVID time. Outcomes will also offer insights on how to utilize the shopping behavior change during the pandemic to reshape downtown areas of cities with a focus on promoting sustainable travel behavior.