This study conducted big data analysis using text mining to investigate Starbucks’ perception of greenwashing and social issues. Starbucks’ greenwashing-related keywords were collected through portal sites such as Naver, Daum, Google, and Big Kines’ newspapers, and 11,522 keywords were derived using Textom. The results of analyzing UCINET 6 and NetDraw as the top 50 keywords are as follows. First, as a result of the degree centrality analysis, the centrality was high in the order of ‘reusable cup’, ‘eco-friendly’, ‘paper straw’, ‘plastic’, ‘use’, ‘controversy’, ‘reusable container’, and ‘marketing’. Second, the eigenvector was influential in the order of ‘reusable cup’, ‘eco-friendly’, ‘paper straw’, ‘plastic’, ‘use’, ‘controversy’, ‘multiple containers’, ‘event’, and ‘environment’. In this study, the results of clustering through CONCOR analysis were classified into four groups: ‘brand image’, ‘disguised environmentalism controversy’, ‘eco-friendly policy’, and ‘reusable cup evaluation’. In this study, Starbucks’ perception of greenwashing and issues were identified, and it is expected that it will be provided as a guideline for companies to build effective marketing strategies for sustainable growth by referring to basic data on eco-friendly management in the future.