Machine Learning-Enhanced Text Mining as a Support Tool for Research on Climate Change

Abstract

In this chapter, the authors explore the theoretical and practical aspects of using text mining approaches supported by machine learning for the automatic interpretation of bulk literature on a contemporary issue—that of climate change risk analysis. The strengths, weaknesses, and opportunities associated with these approaches are investigated. Text mining provides a way to automate and enhance the analysis of text data. However, contrary to popular belief, text mining analysis is not a completely automated process. As with computer-assisted (or -aided) qualitative data analysis software (CAQDAS), it is an iterative method requiring input from a researcher with expert knowledge and a deliberate approach to the analysis. Given the heterogeneity that generally characterizes climate disclosures, the authors postulate that hybrid methodologies are ideal for analysing textual data related to climate change discourse. The authors also demonstrate that text mining is an open and evolving field, in the sense that it can be combined with other approaches to shed new light on the climate discourse.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 2 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.