Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation

TL;DR

This paper presents a method combining Natural Language Processing and network analysis to automate and interpret structured literature reviews, demonstrated through farmers' climate change adaptation studies.

Contribution

It introduces an approach that uses NLP and networks to synthesize literature, linking findings to key disciplinary concepts for faster, interpretable reviews.

Findings

NLP and networks enable quick literature synthesis.

Results are interpretable when backed by theory.

Method applied successfully to climate change adaptation literature.

Abstract

The fast-growing number of research articles makes it problematic for scholars to keep track of the new findings related to their areas of expertise. Furthermore, linking knowledge across disciplines in rapidly developing fields becomes challenging for complex topics like climate change that demand interdisciplinary solutions. At the same time, the rise of Black Box types of text summarization makes it difficult to understand how text relationships are built, let alone relate to existing theories conceptualizing cause-effect relationships and permitting hypothesizing. This work aims to sensibly use Natural Language Processing by extracting variables relations and synthesizing their findings using networks while relating to key concepts dominant in relevant disciplines. As an example, we apply our methodology to the analysis of farmers' adaptation to climate change. For this, we perform a Natural Language Processing analysis of publications returned by Scopus in August 2022. Results show that the use of Natural Language Processing together with networks in a descriptive manner offers a fast and interpretable way to synthesize literature review findings as long as researchers back up results with theory.