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Analysis of citizens’ opinions on bills

Brazil - Chamber of Deputies

Use case ID: 023

Author: Chamber of Deputies of Brazil

Date: 24 June 2024

Objective:

Read citizens’ comments on a bill, as expressed in e-polls and other participatory channels, in order to identify and categorize the main arguments for or against the bill.

Actors:

  • Citizens publicly expressing their opinions
  • MPs
  • Communication (or engagement) unit staff
  • AI system designed for semantic clustering, optionally integrated with sentence segmentation, stance detection and sentiment analysis

Prerequisites:

  • Database correlating citizens’ comments with associated bills
  • Automated text indexing or vectorization process, encompassing proper sentence segmentation, tokenization and semantic representation
  • Database to store user feedback needed to improve the AI system
  • Integration with digital services commonly used by MPs and communication staff

Scenario:

  1. Citizens express their opinion on a specific bill.
  2. Periodically, the AI system breaks down citizens’ comments into smaller sentences, subsequently creating clusters that share similar semantics and stance, whether positive or negative.
  3. An MP or communication staff member selects a bill (or its corresponding e-poll) to view the clusters generated by the AI system.
  4. The AI system offers graphical and list-based visualizations of the sentence clusters. Examples of graphical resources include word clouds and 3D spatial distributions of the sentences.
  5. An MP or communication staff member provides feedback on the AI-generated clusters.

Alternate flows:

  • When necessary, the user can adjust the number of clusters the AI system generates.

Expected results:

  • The process of analysing citizens’ comments on bills is more efficient.
  • The time needed to analyse citizens’ comments on bills is reduced.
  • AI system results are continuously enhanced through the accumulation of feedback over time.

Potential challenges:

  • Ensuring continuous improvement of the AI system over time across various scenarios, including controversial bills and e-polls with few comments
  • Addressing performance and memory issues during sentence vectorization (text embeddings) and cluster reduction
  • Encouraging users to provide feedback

Data requirements:

  • New comments on a given bill require the recreation of the corresponding clusters
  • Periodic verification of AI system performance

Integrations with other systems:

  • Digital services commonly used by MPs and communication staff
  • Comment moderation tool
  • Analytics and reporting tools

Success metrics:

  • Amount of feedback provided by users of the AI system
  • Visual inspection, a qualitative technique, allows for the visualization of clustering results using 2D or 3D graphics

 

The Use cases for AI in parliaments collection is published by the IPU’s Centre for Innovation in Parliament as part of the Parliamentary Data Science Hub’s project to create guidelines for AI governance in parliaments.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence. It may be freely shared and reused with acknowledgement of the author and the IPU. 

A use case describes how a system should work. It is used to plan, develop and measure implementation. A use case is not the same as a case study, which is a descriptive text of an actual project’s implementation. Please note that this use case is provided “as is” and neither the IPU nor the author accepts any responsibility for its use.

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