Public Comment Data
Millions of comments submitted on federal regulations -- the civic participation data that NLP models parse for sentiment.
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Find Me This Data →Overview
What Is Public Comment Data?
Public Comment Data comprises millions of comments submitted on federal regulations, representing civic participation and public opinion on proposed government policies. This dataset is rich with natural language that NLP models and researchers use to analyze sentiment, gauge public support or opposition, and understand stakeholder perspectives on regulatory changes. The data reflects genuine citizen engagement with the rulemaking process and serves as a critical input for policy analysis, corporate compliance tracking, and machine learning applications focused on understanding regulatory impact and public sentiment.
Market Data
8.1%
Market Data Spend Growth (2024)
Source: Substantive Research / Expand Research
18%
ESG Data Spend Increase (2024)
Source: Substantive Research / Expand Research
$2T to $3T+ (2023–2024)
Private Credit AUM Growth
Source: McKinsey / Substantive Research
Who Uses This Data
What AI models do with it.do with it.
Policy Research & Analysis
Government agencies, think tanks, and policy organizations analyze public comments to understand stakeholder concerns and sentiment on proposed regulations.
NLP & Machine Learning Training
AI companies and researchers use comment datasets to train sentiment analysis models, extract regulatory themes, and build classifiers for policy impact prediction.
Corporate Compliance & Strategy
Financial institutions and regulated firms monitor comments on rules affecting their industry to anticipate regulatory shifts and adjust compliance strategies.
Advocacy & Stakeholder Engagement
Non-profits, industry associations, and advocacy groups track comment submissions to identify allies, opposition patterns, and emerging concerns in the regulatory process.
Pricing depends on the proposed terms
We do not have a verified, comparable price for your dataset. Consider the permitted uses, license term, exclusivity, provenance, coverage and quality. A seller asking price is a proposal, not an appraisal or guaranteed sale. Compare prices only when the source, date, currency, unit and license scope are known.
What Buyers Expect
What makes it valuable.valuable.
Completeness & Coverage
Full or representative samples of submitted comments from all major federal rulemaking periods; clear documentation of what is and is not included.
Metadata & Context
Associated regulatory reference (CFR citation, docket ID), submission date, commenter type (individual, organization, government), and regulatory agency for filtering and analysis.
Format & Accessibility
Clean, structured text (CSV, JSON, or database format) that is easy to parse; minimal encoding errors or OCR artifacts; consistent handling of duplicates and spam comments.
Timeliness & Updates
Timely delivery of newly submitted comments or refreshed datasets that align with regulatory cycles; clear versioning and change logs for reproducible research.
Potential applications and organizations
Who's buying.buying.
Analyze sentiment and themes in comments to publish reports on regulatory priorities and public opinion shifts.
Acquire comment datasets to train and benchmark NLP models for sentiment analysis, entity recognition, and policy classification.
Monitor regulatory comments to track emerging issues in fintech, banking, and securities rules affecting their business.
FAQ
Common questions.questions.
Where does Public Comment Data come from?
Public Comment Data is submitted directly to federal agencies via their rulemaking portals (primarily Regulations.gov). Citizens, organizations, and businesses submit comments during defined comment periods on proposed rules. Agencies archive this data and make it publicly available; data providers aggregate, clean, and license access to researchers, companies, and policy organizations.
What formats are typically available?
Providers offer structured formats such as CSV, JSON, or database dumps that include the comment text, submission date, commenter metadata (when disclosed), regulatory docket ID, and agency. Some offerings include pre-processed fields (e.g., sentiment tags, entity labels) or API access for real-time or filtered queries.
How is this data used for NLP and machine learning?
Comment datasets serve as training corpora for sentiment analysis, text classification (e.g., identifying pro/anti-regulation sentiment), named entity recognition (extracting regulatory references and stakeholder names), and topic modeling. The natural language variation and real-world regulatory context make it valuable for fine-tuning domain-specific models.
What are typical licensing terms?
Licensing varies widely based on use case, volume, and user count. Academic and non-profit uses may have favorable terms or free access; commercial machine learning applications typically command higher fees. Providers often impose restrictions on redistribution or require separate licensing for derived products or models.
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