Real-Time Comment Analysis

2001 Roadless Rule Revision

A running read of the public comments submitted on this proposal. Each comment is deduplicated, scored for uniqueness, and classified for its position and topics.

2026-08-202026-08-21 · 3,396 comments to date
3,396
Comments collected
2,782
Unique after dedup
614
Duplicate submissions
2,738
Truly unique
17
Topics tracked
3,346
Classified for sentiment

Where commenters stand

Each unique comment classified for its position on the proposed rescission.

Opposes rescission: 3,242 (96.9%)Supports rescission: 85 (2.5%)Neutral / unclear: 19 (0.6%)
Opposes rescission96.9% · 3,242
Supports rescission2.5% · 85
Neutral / unclear0.6% · 19

Comment length distribution

How long the comments run — a proxy for substance versus one-line form submissions.

236
471
706
942
2505007501k1k1k1k2k2k2k2k3k3k3k3k4k4k4k4k5k5k+
Comment length (characters, upper bound of bin)Bar height = number of comments

About the comments

Duplicate and template submissions are collapsed so each distinct comment counts once.

18% duplicate or template
Collectedall submissions
3,396
Unique textafter exact dedup
2,782−614 copies
Truly uniqueafter middle-content pass
2,738−44 near-copies

The topics people are commenting about

Comments per topic (a comment can raise several).

Environmental Protection Biodiversity
3,163
Public Land Access Rights
2,695
Recreation Tourism Public Use
1,115
Water Quality Quantity
1,052
Forest Management Wildfire
767
National Security Public Safety
560
Tribal Sovereignty
398
Economic Impact Fiscal
328
Resource Development Extraction
256
Climate Carbon Storage
237
Wildlife Habitat
154
Public Health Wellbeing
126

Sharing & methodology

These results are meant to be shared.

Please cite us. This analysis is the intellectual property of Roadless.org and Nicholas Holshouser. You are welcome to reproduce it — every reproduction must include this citation:
Roadless.org and Nicholas Holshouser, “2001 Roadless Rule Revision — Public Comment Analysis,” https://roadless.org.

How it works. Sentiment and topic classification is performed by a large language model (an open-weight Qwen 2.5 model) that reads each unique comment and labels its position — supports, opposes, or neutral — and the topics it raises. Lexical analysis, which finds duplicate and template submissions and scores each comment for originality, uses TF-IDF and n-gram phrase matching, sentence-transformer embeddings, and syntactic and stylometric features (built with spaCy, scikit-learn, and sentence-transformers).