The outbreak of the COVID-19 virus in 2020 marked the beginning of a tide of racist sentiment against Asian Americans in the United States. Spurred on by the Trump administration’s use of xenophobic slurs to refer to the virus, anti-Asian sentiment dominated the national conversation about the epidemic and fueled a dramatic increase in hate crimes (Covid “hate Crimes” against Asian Americans on Rise - BBC News, 2021). Much research is still being conducted to uncover the extent of the psychological, physical, and financial harm incurred by Asian Americans at this time. The restaurant industry is one sector that faced uniquely severe circumstances; loss of business during lockdown was compounded by mounting social stigma. In this study, I offer a potential framework for investigating negative public sentiment using average sentiment scores from Yelp reviews for restaurants in the Chicago area. I employ an exploratory spatial data analysis approach to 1) identify potential patterns in the spatial distribution of negative sentiment and 2) determine which communities contain restaurants that experienced highly negative and subjective reviews, indicating the presence of anti-Asian sentiment. As explained by Intergroup Threat Theory, there are a multitude of social, situational, and structural factors that contribute to anti-Asian prejudice. Among such factors are certain demographic characteristics associated with a higher risk of engaging in prejudicial behavior or buying into fake news content targeting Asian-Americans (Lambe et al. 2021, Tan et al. 2021, Wright & Duong 2021). Through a spatially lagged-X regression model using data from the American Community Survey and the Chicago Data Portal, I examined associations between neighborhood demographic contexts and instances of negative sentiment within Yelp reviews. Results of each regression identified trends consistent with the predictors of Anti-Asian prejudice, most notably lower educational attainment.