Big Data and Machine <i>Learning</i> in <scp>ESG</scp> Research*

Abstract The wide applications of machine learning techniques to big data allow researchers to dig deep into novel large‐scale data sets, such as job postings, earnings calls, and news reports. They also equip researchers with powerful tools to study important but subtle/challenging topics that are impossible to explore before on a large scale, such as corporate culture and climate risk exposure. In this review, I survey various applications of different machine learning techniques in ESG research, beginning with foundational methods such as bag‐of‐words, progressing through topic modeling, word embedding, and BERT, and culminating with generative artificial intelligence (AI) and other advanced machine learning approaches. I conclude by outlining future directions for using big data and machine learning in ESG research.