Unlocking insights into the green hydrogen industry through natural language processing techniques

This case study illustrates how natural language processing (NLP) techniques, including web scraping and text mining, were instrumental in identifying emerging technologies and crucial skills for integrating green hydrogen into South Africa's energy sector. The Department of Science and Innovation (DSI) collaborated with the University of Johannesburg to explore clean energy innovation and recognise the importance of green hydrogen in shaping a sustainable energy future. Led by Professor Arnesh Telukdarie, the Professor of Digital Business and Research Chair at Johannesburg Business School (JBS), a team proficient in web scraping techniques gathered extensive green hydrogen energy data from various online sources, including research articles, industry reports, and policy documents. This diverse and extensive dataset covers multiple facets of the green hydrogen value chain. Subsequently, through text mining, they analysed this data to reveal patterns, trends, and essential skills crucial for the sector's growth. The insights derived from this approach provide practical recommendations for addressing the skills gap and fostering innovation in clean energy. This methodology aims to empower South Africa's energy sector by facilitating informed decision-making and nurturing a culture of sustainability and innovation within the evolving landscape of green hydrogen.