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1	AI augmented approach to identify shared ideas from large format public consultation
	Weng, Min-Hsien; Wu, Shaoqun; Dyer, Mark. - : MDPI AG, 2021
	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.
	Keyword: city; computer science; data analysis; natural language processing; public participation; software engineering; urban planning
	URL: https://hdl.handle.net/10289/14537 https://doi.org/10.3390/su13169310
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2	Urban Narrative: Computational Linguistic Interpretation of Large Format Public Participation for Urban Infrastructure
	Dyer, Mark; Weng, Min-Hsien; Wu, Shaoqun...
	In: Urban Planning ; 5 ; 4 ; 20-32 ; The City of Digital Social Innovators (2020)
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3	Urban narrative: Computational linguistic interpretation of large format public participation for urban infrastructure
	Dyer, Mark; Weng, Min-Hsien; Wu, Shaoqun. - : Cogitatio, 2020
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4	The JStar language philosophy
	Utting, Mark; Weng, Min-Hsien; Cleary, John G. - : Elsevier BV, North-Holland, 2014
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5	The JStar language philosophy
	Utting, Mark; Weng, Min-Hsien; Cleary, John G.. - : Elsevier, 2014
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6	The JStar language philosophy
	Utting, Mark; Weng, Min-Hsien; Cleary, John G.. - : Association for Computing Machinery (ACM), 2013
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7	The JStar language philosophy
	Utting, Mark; Weng, Min-Hsien; Cleary, John G.. - : University of Waikato, Department of Computer Science, 2013
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8	The JStar language philosophy
	Utting, Mark; Weng, Min-Hsien; Cleary, John G.. - : The Association for Computing Machinery, 2013
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