The literature on climate change research has evolved tremendously since the 1990s. The goal of this study is to use text mining to review the climate change literature and study the evolution of the main trends over time. Specific keywords from articles published in the special issue  Industrial Ecology for Climate Change Adaptation and Resilience in the Journal of Industrial Ecology are first selected. Details of over 35,000 publications containing these keywords are downloaded from the Web of Science from 1990 to 2018. The number of publications and co-occurrence of keywords are analyzed. Moreover, latent Dirichlet allocation (LDA)-a probabilistic approach that can retrieve topics from large and unstructured text documents-is applied on the abstracts to uncover the main topics (consisting of new terms) that naturally emerge from them. The evolution in time of the importance of some emerging topics is then analyzed on the basis of their relative frequency. Overall, a rapid growth in climate change publications is observed. Terms such as climate change adaptation appear on the rise, whereas other terms are declining such as pollution. Moreover, several terms tend to co-occur frequently, such as climate change adaptation and resilience. The database collected and the LiTCoF (Literature Topic Co-occurrence and Frequency) Python-based tool developed for this study are also made openly accessible. This article met the requirements for a gold - gold JIE data openness badge described .
Dayeen, FR; Sharma, AS; Derrible, S
A text mining analysis of the climate change literature in industrial ecology
Journal Of Industrial Ecology