• Angvarrah Lieungnapar Faculty of Humanities and Social Sciences, Suan Sunandha Rajabhat University, Bangkok, Thailand
Keywords: Key-BNC, Keyword analysis, Topic identification


Since the UN adopted the Sustainable Development Goals (SDGs) in 2015, SDG concepts have been integrated in several fields including education. For EFL lecturers, a simple way to allow their students adopt SDG concepts is through reading documents related to SDGs. However, identifying reading topics related to SDG concepts is problematic since the concept is too broad and the available SDG keywords are limited. Under the assumption that keywords and their clusters (multi-word terms) based on co-occurrence can reflect the topics of the content, this study aims to develop a list of clusters of SDG keywords on the basis of Log-likelihood (LL), a statistical calculation which generate keyness values based on frequency of occurrence. Reports, explanations and descriptions related to the 17 SDG goals were downloaded from UN Sustainable Development websites. The keywords were identified by Key-BNC, which is a online application providing a simple interface for calculating comparative keyword statistics against a word list from the British National Corpus (BNC). The AntConc program was used to identify clusters of keywords. Most of the keywords found were obviously related to each SDG goal: poverty (SDG1), hunger (SDG2), health (SDG3), and education (SDG4). Some of them were indirectly but relatively related to the goals: pandemic (SDG1), wasting (SDG2), global (SDG3) and closures (SDG4). The results of clusters of keywords can be applied to identify more specific topics related to each SDG goal.