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- W4385584877 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." @default.
- W4385584877 created "2023-08-05" @default.
- W4385584877 creator A5025616136 @default.
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- W4385584877 date "2023-06-30" @default.
- W4385584877 modified "2023-10-17" @default.
- W4385584877 title "Machine Learning-Enhanced Text Mining as a Support Tool for Research on Climate Change" @default.
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- W4385584877 doi "https://doi.org/10.4018/978-1-6684-8634-4.ch004" @default.
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