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The purpose of this study was to analyze research trends in English vocabulary learning. From 2011 to 2022, two hundred and six articles were collected through Biblio Data Collector by Netminer and the information about the publication year, authors, keywords, abstracts was analyzed by text mining. Text mining is a tool for objectively analyzing atypical data like articles and figuring out key words. Frequency analysis and centrality analysis (degree centrality, closeness centrality, and betweenness centrality) were conducted. Therefore, more diversified approaches for analyzing data were obtained. Finally, topic modeling analysis was carried out for extracting potential themes. The findings are following: at first, the words that appeared the most frequently in data were ‘learner (297)’, ‘effect (288)’, ‘strategy (282)’, ‘result (244)’, and ‘level (198)’. Secondly, the theme extracted from Topic Modeling was about ‘learning vocabulary in a more practical way‘. According to the result, educational implication was discussed.