Deep (Learning) State How Romania is using NLP as a political advisor.

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Romania's flags as a neural network

Meet the Romanian government’s automated political adviser.

What’s new: The Prime Minister of Romania launched ION, a system that summarizes and organizes public comments for cabinet ministers, reported.

How it works: Romanian citizens can submit comments via a website or by embellishing Twitter, Facebook, and Instagram posts with the tag @noisuntemION (“we are ION”). An online document describes the system in detail.

  • The system uses an unsupervised semantic similarity model to prioritize comments depending on whether they’re relevant to national or international affairs.
  • A natural language model extracts each comment’s topic (government activity, economics, healthcare, energy, sports, and so on) and references to people, locations, or events. A sentiment analyzer determines whether a comment is positive or negative and how strongly it expresses an opinion.
  • A clustering algorithm groups similar messages; for instance, all messages that express a particular sentiment about a specific issue. The system generates a succinct description of each cluster.
  • Another clustering algorithm maps relationships between clusters and creates superclusters. For instance, an issue’s supercluster may contain clusters that collect different sentiments.
  • A subsystem monitors the clusters for changes. Officials can check the system for significant changes that may inform policy decisions.

Behind the news: Governments use AI to manage operations, dispense benefits, and administer justice. However, systems that influence policy remain largely experimental. For instance, Salesforce engineers trained a model to create a tax policy that promoted general income equality and productivity more effectively than the current United States tax code.

Why it matters: Politicians and policymakers must often interpret the will of the people through polls, press reports, or lobbyists. Romania’s experiment may tell officials more directly what constituents want.

We’re thinking: Many companies analyze social media to understand customer sentiment; for instance, clustering tweets to see what people are saying about a brand. Policymakers' embrace of a similar approach is a welcome step.


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