We worked with a team from the Ministry for the Environment and Ackama to enhance the ministry’s submission processing and analysis tool, Croissant.

Public submissions are many and varied

As part of law making in New Zealand, the public may be invited to make submissions on proposed legislation. Information gathered from submissions guides decision-making and improves government policies.

Some consultations attract tens of thousands of submissions, which all have to be analysed. The responses vary in form from templated submissions to email to handwritten documents. At the end of a consultation, a summary report is produced.

About Croissant

Croissant was built by the Department of Internal Affairs, the Ministry for the Environment (MfE) and Ackama to streamline and standardise the analysis of submissions. It holds all the submissions related to each consultation and allows their text to be analysed. While robust, efficient and useful in its current form, MfE is continually working to improve the tool.

Croissant was designed to be an all-of-government platform. It is currently hosted by MfE and has been made available to a limited number of other agencies. Its use could be extended to all government agencies in the future.

Tagging submissions for analysis

Within Croissant, submission text is tagged with keywords that reflect its content. Tags highlight themes mentioned in each submission so they can be clearly identified, then sorted and analysed. Tagging also allows submissions from specific groups (such as companies, iwi or regions) to be identified and considered appropriately.

The Croissant dashboard shows the status of different consultations, the submissions themselves and how much of the text has been tagged. Information about the number of tags applied, alongside examples of tagged text can be produced to add to the consultation report.

Meeting a niche need

Applying tags to submission text within Croissant has historically been a slow, manual process. MfE identified a need to improve this process but found no suitable applications – only expensive, stand-alone tools lacking transparency were available.

Dragonfly Data Science was then approached to develop a machine learning (artificial intelligence) module to automate the tagging. What we built fits with Croissant’s existing information architecture. It processes text automatically in parallel with the manual tagging.

Module operates in two different modes

In an unsupervised mode, the machine learning module identifies groups of submissions that contain similar ideas. The main themes within each group are also identified.

This mode provides an overview of a large body of text without requiring human intervention. The module creates an interactive map of all submissions, with similar submissions grouped together. These results can help to develop tags that are appropriate for all the submission text.

In a supervised learning mode, the module is taught to associate manually applied tags with specific text under review. It then automatically applies those same tags to the remaining untagged text.

Automatic tagging can be triggered at any time during the manual text processing. After tagging, the text can be analysed by a variety of other programmes.

Security and transparency were essential

Croissant is helping to facilitate genuinely democratic public processes. The use of open source libraries and data formats means that the results are easily accessible by analysts rather than being locked away in a proprietary format. The module is also structured in a way that guarantees the security of every submitter’s personal information.

Low cost allows for more opinions

Croissant has a low running cost compared with some other submission analysis tools. This makes it possible for the government and other organisations to gather public opinion on a wider range of topics.

Using a machine learning approach saves time by enabling a faster turnaround from the close of submissions to the consultation report. Also, because this automated approach easily scales to manage large quantities of text, every single submission can be analysed, and no idea is left behind.

A good, imperfect solution and next steps

The solution we developed is not perfect, or intended to be perfect. However, MfE estimates that using our module is saving about 80–90 percent of the time taken to manually tag submissions.

It is also anticipated that the project will have paid for itself after 12 consultations because of the faster tagging, analysis and report production.

Machine learning opens up a wide range of possible future improvements like being able to include audio submissions or submissions in other languages. We are working on both of these ‘wish list’ items.

A future enhancement is to provide text analysis inside the tool itself, which would enable high level analysis to be presented in the consultation report.

Project team

Richard Mansfield, Ignatius Menzies, Yvan Richard, Finlay Thompson.

More information

Read a story about the initial development and funding of Croissant.

I found Dragonfly’s intent and motivation was aligned with what we were trying to do. It felt like they partnered with us on a common objective rather than being vendors for part of a tool I was building.

Their work was timely. We needed things at certain times and they were able to work to that – I didn’t have to worry about delivery.

Dragonfly’s approach was flexible. They adapted to what we were trying to do rather than having to do it their way. There are some access constraints in the tool but they were happy to work around that – there was no drama.

There’s no question that we’ll work with Dragonfly to refine Croissant.

Elias Wyber
Product owner
Ministry for the Environment .