AI-Powered Sentiment Analysis for Public Feedback

Consultations often include large amounts of open-ended responses, making it difficult to quickly identify sentiment and key themes. By integrating AI-powered sentiment analysis (e.g., AWS Comprehend, Google Natural Language API), Citizen Space could:

  • Automatically analyze public sentiment (positive, neutral, negative)
  • Highlight recurring themes and concerns
  • Flag potentially inappropriate content for moderation

Example: Sports Integrity NZ implemented an AI-driven sentiment analysis tool to help categorize feedback and reduce manual data processing time.

Why the contribution is important

Without automation, teams must manually sift through responses, which can lead to delays and missed insights. AI-powered sentiment analysis would:
✅ Speed up data analysis and highlight key trends
✅ Ensure fairer decision-making by identifying sentiment patterns
✅ Support moderation efforts by flagging inappropriate responses

Do you think sentiment analysis would improve your consultation process? Let us know your thoughts or submit your own idea!

by admin on February 11, 2025 at 10:16AM

Current Rating

Average rating: 5.0
Based on: 3 votes

Comments

  • Posted by SineadMacauley February 27, 2025 at 09:38

    From my research, it seems like this feature is increasing in demand at central and federal levels. The main priority from the supplier perspective will have to be thoroughly auditing the sentiment analysis tool's security and bias tendencies. Ideally, I think any sentiment integration would need to show a percentage of confidence in the results presented.

  • Posted by HamishW March 03, 2025 at 10:20

    I'd be interested to know what is available already as I know the NZ Govt has used Croissant (a local in house development) and some other more open source ones (llangchain being used by the UK govt).

    There are terrific models like Claude Sonnet 3.7 out there that can do amazing things and we can access them via AWS Bedrock however it would be great to see some 'safe' models in use.
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