AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation

Min-Hsien Weng, Shaoqun Wu and Mark Dyer
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Min-Hsien Weng: School of Engineering, The University of Waikato, Hamilton 3216, New Zealand
Shaoqun Wu: School of Computing and Mathematical Sciences, The University of Waikato, Hamilton 3216, New Zealand
Mark Dyer: School of Engineering, The University of Waikato, Hamilton 3216, New Zealand

Sustainability, 2021, vol. 13, issue 16, 1-19

Abstract: Public data, contributed by citizens, stakeholders and other potentially affected parties, are becoming increasingly used to collect the shared ideas of a wider community. Having collected large quantities of text data from public consultation, the challenge is often how to interpret the dataset without resorting to lengthy time-consuming manual analysis. One approach gaining ground is the use of Natural Language Processing (NLP) technologies. Based on machine learning technology applied to analysis of human natural languages, NLP provides the opportunity to automate data analysis for large volumes of texts at a scale that would be virtually impossible to analyse manually. Using NLP toolkits, this paper presents a novel approach for identifying and visualising shared ideas from large format public consultation. The approach analyses grammatical structures of public texts to discover shared ideas from sentences comprising subject + verb + object and verb + object that express public options. In particular, the shared ideas are identified by extracting noun, verb, adjective phrases and clauses from subjects and objects, which are then categorised by urban infrastructure categories and terms. The results are visualised in a hierarchy chart and a word tree using cascade and tree views. The approach is illustrated using data collected from a public consultation exercise called “Share an Idea” undertaken in Christchurch, New Zealand, after the 2011 earthquake. The approach has the potential to upscale public participation to identify shared design values and associated qualities for a wide range of public initiatives including urban planning.

Keywords: natural language processing; data analysis; public participation; city; urban planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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