TL;DR
This paper introduces an open-source NLP pipeline for extracting keywords from open-ended survey responses, enabling ethical, transparent, and scalable analysis of human opinions beyond traditional methods.
Contribution
The authors develop a novel, open-source computational pipeline that outperforms existing systems in extracting representative keywords from survey data, emphasizing transparency and bias detection.
Findings
Pipeline exceeds state-of-the-art performance
Ensures transparency and bias scrutiny
Applicable to large-scale open-ended survey data
Abstract
Open-ended survey data constitute an important basis in research as well as for making business decisions. Collecting and manually analysing free-text survey data is generally more costly than collecting and analysing survey data consisting of answers to multiple-choice questions. Yet free-text data allow for new content to be expressed beyond predefined categories and are a very valuable source of new insights into people's opinions. At the same time, surveys always make ontological assumptions about the nature of the entities that are researched, and this has vital ethical consequences. Human interpretations and opinions can only be properly ascertained in their richness using textual data sources; if these sources are analyzed appropriately, the essential linguistic nature of humans and social entities is safeguarded. Natural Language Processing (NLP) offers possibilities for meeting…
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