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This paper proposes a text analytics approach using dictionary-based clustering, word counting data, and discrete regression modeling to study the relationship between demand behaviors and supply issues affecting supply chain resilience during the first stages of the COVID-19 pandemic. The work used news and media articles gathered from the LexisNexis database covering 5 months between February and July 2020. The data analyses describe the general patterns in the news texts by using text mining techniques, and the methodology describes the relationship between consumer behavior, supply chain issues, and the reduced level of service shown during the study period. Demand behaviors include precautionary and opportunistic buying, which affected many countries and could be the result of a lack of perceived control and other factors; for example, near-empty shelves of certain products could have prompted consumers to increasingly look for comparable products, driving up demand. Additionally, the method explored the potential effect of strategies to mitigate impacts on the resilience of supply chains. The results confirmed that buying behaviors and a reduction in the capacity of the supply chain led to a lower level of service being perceived by consumers, however, resilience strategies were found to mitigate the impact of such capacity reductions. Empirical analyses showed that the proposed approach, using data extracted from the news, could identify and represent impacts consistent with expectations from the supply chain field under disruptions, and quantify the magnitude of the impacts as the pandemic evolved, providing more information for decision making.