How AI can help to analyse public feedback
For many local authorities, success is a double-edged sword.
You launch a project, get 5,000 comments, and suddenly realise you don't have the staff hours to read them all. Your team is stretched as it is, and with resources squeezed, dealing with masses of data manually can be time-consuming and prone to inconsistencies.
This 'analysis paralysis' often means valuable insights sit gathering digital dust.
AI can lighten the load by automatically organising, summarising, and revealing patterns in feedback at scale, enabling government teams to respond effectively to their communities’ needs.
Tagging feedback with AI
‘Tagging’ refers to a process that organises and labels feedback into meaningful categories.
Instead of staff having to spend hours going through every submission with a fine tooth comb, AI can scan the text and labels it with themes (for example, ‘safety,’ ‘transportation’, or ‘recreation’), sentiment, or locations.
This makes it easier for your team to spot trends, identify patterns, and understand the collective sentiment on an issue. With tagging, processes that would take human analysts days or weeks now take minutes or hours.
Automatic vs. human-steered tagging
Automatic tagging
- What it is: AI independently assigns tags to feedback and responses based on detected patterns in the text without human intervention.
- Benefits: It processes large volumes of data instantly, eliminates manual effort (and therefore resource allocation), maintains consistency, and removes human bias from the initial categorisation.
- Use case: When you need rapid first-pass analysis of high-volumes of feedback, or want to identify unexpected or missed themes.
Human-steered tagging
- What it is: AI assigns tags to feedback using guidance or rules provided by humans. The AI does the heavy lifting, but staff can adjust tags, refine categories or train the system to recognise community-specific details.
- Benefits: It ensures the categorisation aligns with the project’s priorities, stays sensitive to the local context and terminology, and builds staff confidence in the results.
- Use case: When feedback relates to sensitive topics, is from communities with strong dialects or multiple languages, or requires alignment with specific policy frameworks.
Tagging for topics and sentiment
Topic tagging groups feedback by subject matter – for example, ‘transportation’, ‘housing affordability’, and ‘utilities’ – to determine what community members are discussing and how popular certain topics are.
Sentiment tagging identifies the emotional tone: positive, negative, neutral, or urgent. This helps governments prioritise the issues that require immediate attention. It can also be used to determine the efficacy and popularity of previous projects or policy.
Combining both topic and sentiment gives a complete picture: what the community is talking about and how they feel about it, enabling both thematic and emotional understanding in little time.
Summarising large volumes of feedback
AI can generate concise, easy-to-read summaries of hundreds or thousands of comments, distilling key points without losing nuance. This helps decision-makers quickly grasp community concerns without having to read every individual response, removing the human risk of information fatigue that can lead to important information falling through the cracks.
It also preserves access to the original feedback – this is especially useful for sensitive or complex topics that might involve deeper investigation.
Surfacing trends and hidden connections
AI doesn’t just organise feedback, it studies it. Revealing patterns that aren’t immediately obvious to human reviewers, such as:
- Trend detection: By identifying recurring themes across time periods, neighborhoods, or demographic groups – spotting emerging issues before they escalate.
- Hidden connections: Discovering correlations between different responses. For instance, AI might reveal that residents complaining about 'traffic' in Zone A are actually talking about 'school safety' during pick-up hours; a nuance that simple keyword searching would miss.
- Cross-question insights: Revealing how answers to one question can predict responses to another (e.g. residents who support Question 1's bike lane proposal are more likely to prioritise pedestrian infrastructure in Question 2).
- Clustering unexpected themes: Grouping similar ideas together even when different words are used, surfacing concerns that weren't anticipated in the initial categories.
- Predictive patterns: Using historical engagement data to forecast future community needs, enabling proactive planning.
Choosing the right AI tools for community engagement
Use specialised GovTech platforms for analysis
When analyzing public feedback, it’s important to use purpose-built government engagement platforms rather than general AI chatbots like ChatGPT, CoPilot, or Gemini.
Go Vocal uses "closed-context AI". This means the AI in our platform only analyzes the data you provide – your survey responses and resident comments – without inventing facts from external sources. This eliminates hallucinations (when AI makes up false or inaccurate information), protects resident data with strong privacy and security standards, and provides clear audit trails for transparency.
Plus, it keeps all the processing in a secure, controlled environment, making closed-context AI a far better fit for public sector work.
Bridge the digital divide
As local governments increasingly adopt hybrid engagement strategies, a new challenge is emerging: data fragmentation.
When you combine digital surveys with town hall sticky notes and paper feedback forms, valuable input often gets trapped in physical silos, making it difficult to see the full picture.
This fragmentation forces staff to manually transcribe hundreds of cards or risks leaving valuable offline voices out of the final analysis.
Go Vocal solves this with Form Sync, a unique feature to instantly digitizse handwritten input.
By bringing offline data directly into your platform, you can analyze every voice – whether submitted at a town hall or on a tablet – in one unified view, ensuring a truly inclusive single source of truth.
General AI tools for visualisation
After you’ve analyzed and exported your findings from a secure platform, AI tools like ChatGPT, Gemini, or Canva can help generate compelling charts and visualisations to communicate the results to stakeholders. The platforms are intuitive, easy to use, and can work from text prompts, so you don’t need a design degree.
Ensuring ethical AI use in community engagement
AI is an undeniably useful tool, and it’s revolutionising how governments are connecting with their communities. However, it’s important to stress that it is just a tool, and will never replace human-to-human engagement and empathy.
AI comes with its faults, and isn’t wholly trusted by all, so implementing it within governance comes with a responsibility to use it ethically and thoughtfully.
Best practices checklist:
Here are some best practices to ensure AI-driven insights are secure, unbiased, transparent, and aligned with the community’s values:
- Data privacy safeguards: Ensure community data is stored securely, anonymised where appropriate, and complies with privacy regulations; use de-identification and strict data retention rules.
- Bias mitigation: Regularly audit AI outputs to identify systematic underrepresentation of marginalised communities; weight feedback appropriately to ensure balance by territory, language, and channel.
- Transparency practices: Explain clearly how AI categorises and prioritises feedback; make methodology accessible to the public using plain language.
- Human oversight: Maintain staff review of AI-generated insights at defined checkpoints; validate findings against local knowledge and community input with documented criteria.
- Community co-design: Involve residents in defining what constitutes priority issues and validating AI findings to ensure cultural and value alignment.
- Traceability: Maintain clear connections from summaries to original comments so anyone can verify the source and audit decisions.
Making AI work in your engagement process
Getting large amounts of public feedback is a fantastic thing, and AI can make dealing with it less daunting.
Use it to create clear, actionable insights in a fraction of the time, spotting trends, clustering ideas, and gauging sentiment without gobbling up resources – or missing nuances.
Go Vocal allows governments to blend smart AI systems with human insight to create transparent, accessible, effective and meaningful engagement, creating healthier, happier communities.
Curious how this looks in practice? Explore our features or book a chat with an expert to see our platform in action and discuss your specific engagement challenges.
FAQs about AI for community engagement and feedback analysis
