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# --- python bytecode ---
__pycache__/
*.py[cod]
*$py.class
# --- environment files ---
.env
.env.*
*.local
.venv/
venv/
env/
# --- emacs ---
*~
\#*\#
.\#*
*.elc
# --- project private data ---
/private/
archive/
# --- misc ---
.DS_Store

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# Table of Contents
1. [Project Goals](#org863a759)
2. [Architecture](#orgcd91fd0)
1. [Scraper](#org3256ad3)
2. [Storage](#org7a9a92c)
3. [Analysis](#org6ed72dc)
3. [Roadmap](#org416f14d)
<a id="org863a759"></a>
# Project Goals
1. Document and analyze sentiment of public comments on Virginia law, to determine:
1. the utility of this forum as a mechanism for public comment, and
2. the impact of this forum on Virginia regulation.
2. Make data and insights broadly available.
3. Generalize to other public comment tools.
<a id="orgcd91fd0"></a>
# Architecture
1. Scrape/Parse: ****Scrapy**** for downloading comments
2. Storage: json
3. Sentiment analysis: Claude haiku
4. Display: TBD
<a id="org3256ad3"></a>
## Scraper
Scrapy provides a simple mechanism for browsing and
1. Forums listing page: \`Forums.cfm\` - lists all open forums with agency, reg title, action type, brief description, closing date, comment count
2. Comment listing page: \`comments.cfm?GDocForumID=X\` or \`comments.cfm?stageid=X\` or \`comments.cfm?petitionid=X\` - lists comments with title, author, date
3. Individual comment page: \`viewcomments.cfm?commentid=X\` - shows regulation title + brief description at the top, plus the comment
<a id="org7a9a92c"></a>
## Storage
One JSONL file per forum/bill.
<a id="org6ed72dc"></a>
## Analysis
Google and Amazon both return generic sentiment (tone of writing: positive/negative), not stance (for/against the regulation): "I strongly believe the government should NOT interfere" is negative tone but "against" the regulation. We will run the forum/bill title and cache the entirety of the proposed change, perhaps as a fallback.
<table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides">
<colgroup>
<col class="org-left" />
<col class="org-left" />
<col class="org-left" />
<col class="org-left" />
<col class="org-left" />
<col class="org-left" />
</colgroup>
<thead>
<tr>
<th scope="col" class="org-left">Tool</th>
<th scope="col" class="org-left">Output</th>
<th scope="col" class="org-left">Context</th>
<th scope="col" class="org-left">Sarcasm</th>
<th scope="col" class="org-left">Context window</th>
<th scope="col" class="org-left">Cost/1k comments</th>
</tr>
</thead>
<tbody>
<tr>
<td class="org-left">Google NL API</td>
<td class="org-left">-1→+1, magnitude</td>
<td class="org-left">No/generic</td>
<td class="org-left">Poorly</td>
<td class="org-left">No</td>
<td class="org-left">~$12</td>
</tr>
<tr>
<td class="org-left">Amazon Comprehend</td>
<td class="org-left">Pos/Neg/Neutral/Mixed</td>
<td class="org-left">No/generic</td>
<td class="org-left">Poorly</td>
<td class="org-left">No</td>
<td class="org-left">~$0.10</td>
</tr>
<tr>
<td class="org-left">Claude Haiku</td>
<td class="org-left">Prompted → for/against/neutral</td>
<td class="org-left">Yes</td>
<td class="org-left">Yes, with prompt</td>
<td class="org-left">Yes</td>
<td class="org-left">~$0.100.30</td>
</tr>
<tr>
<td class="org-left">GPT-4o-mini</td>
<td class="org-left">Prompted → same</td>
<td class="org-left">Yes</td>
<td class="org-left">Yes</td>
<td class="org-left">Yes</td>
<td class="org-left">~$0.050.15</td>
</tr>
</tbody>
</table>
<a id="org416f14d"></a>
# Roadmap
1. Scrape one forum
2. Compare sentiment models
3. Display
4. Scrape all data
5. Scale?

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# agent rules
## priorities
- optimize for simplicity, boringness, and long-term maintainability
- prefer minimal diffs; avoid refactors unless required for the active task
## tech stack
- python; scrapy
- file storage: json or csv
- assume local virtual env is available and accessible
- do not add new dependencies unless explicitly approved; if unavoidable, document justification in the active task notes
## workflow
- prefer direct argv commands (no bash -lc / compound shell chains) unless necessary
- work on ONE task at a time unless explicitly instructed otherwise
- at the start of work, state the task id you are executing
- do not start work unless a task id is specified; if missing, choose the earliest unchecked task and say so
- propose incremental steps
- always include basic tests for core logic
- when you complete a task:
- mark it [X] in docs/tasks.md
- fill in evidence with commit hash + commands run
- never mark complete unless acceptance criteria are met
- include date and time (HH:MM)
```
* [ ] t1.1 Task Title (1)
Description and PM notes
** acceptance criteria
1. AC 1
2. AC 2
** notes
- document thoughts, decisions, reasoning
** evidence
- commit:
- tests:
- datetime:
```

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* [ ] t1.1: scrape one forum (1)
Use https://www.townhall.virginia.gov/L/comments.cfm?GDocForumID=452 as the first forum. Scraper should be run manually at this step.
** acceptance criteria
1. run manual scraper
1. store proposal title and description
2. store comment title, commenter, date
3. store relevant metadata
2. friendly/polite scraping
** notes
** evidence
- commit:
- tests:
- datetime:
* [ ] t1.2: initial analysis pipeline
Write a simple pipeline for both - prefer non-concurrent/async from scraping run. Should be run manually, separate from scraper. You may use scrapy, but are not required to.
** acceptance criteria
1. run manual sentiment analysis of selected file against haiku
2. run manual sentiment analysis of selected file against gpt-4o
** notes
** evidence
- commit:
- tests:
- date:

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#+title: VA Townhall
#+date: [2026-05-05 Tue]
#+version: 1
* Project Goals
1. Document and analyze sentiment of public comments on Virginia law, to determine:
1. the utility of this forum as a mechanism for public comment, and
2. the impact of this forum on Virginia regulation.
2. Make data and insights broadly available.
3. Generalize to other public comment tools.
** Document and analyze sentiment
- Scrape the data, parse, clean, and store. Clearly separate scraper from sentiment analyzer for maximum auditability.
- Build tests for identifying abuse, such as spam and account fraud
- Identify any patterns connecting measured sentiment against VA decisions
** Make data available
- Pick a good visualization tool
** Generalize
- Identify scalable ways to apply this toolset to similar problems
* Architecture
1. Scrape/Parse: **Scrapy** for downloading comments
2. Storage: json
3. Sentiment analysis: Claude haiku
4. Display: TBD
** Scraper
Scrapy provides a simple mechanism for browsing and
1. Forums listing page: `Forums.cfm` - lists all open forums with agency, reg title, action type, brief description, closing date, comment count
2. Comment listing page: `comments.cfm?GDocForumID=X` or `comments.cfm?stageid=X` or `comments.cfm?petitionid=X` - lists comments with title, author, date
3. Individual comment page: `viewcomments.cfm?commentid=X` - shows regulation title + brief description at the top, plus the comment
** Storage
One JSONL file per forum/bill.
** Analysis
Google and Amazon both return generic sentiment (tone of writing: positive/negative), not stance (for/against the regulation): "I strongly believe the government should NOT interfere" is negative tone but "against" the regulation. We will run the forum/bill title and cache the entirety of the proposed change, perhaps as a fallback.
| Tool | Output | Context | Sarcasm | Context window | Cost/1k comments |
|-------------------+--------------------------------+------------+------------------+----------------+------------------|
| Google NL API | -1→+1, magnitude | No/generic | Poorly | No | ~$12 |
| Amazon Comprehend | Pos/Neg/Neutral/Mixed | No/generic | Poorly | No | ~$0.10 |
| Claude Haiku | Prompted → for/against/neutral | Yes | Yes, with prompt | Yes | ~$0.100.30 |
| GPT-4o-mini | Prompted → same | Yes | Yes | Yes | ~$0.050.15 |
* Roadmap
1. Scrape one forum
2. Compare sentiment models
3. Display
4. Scrape all data
5. Scale?

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# Define here the models for your scraped items
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html
import scrapy
class ScraperItem(scrapy.Item):
# define the fields for your item here like:
# name = scrapy.Field()
pass

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# Define here the models for your spider middleware
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/spider-middleware.html
from scrapy import signals
# useful for handling different item types with a single interface
from itemadapter import ItemAdapter
class ScraperSpiderMiddleware:
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the spider middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_spider_input(self, response, spider):
# Called for each response that goes through the spider
# middleware and into the spider.
# Should return None or raise an exception.
return None
def process_spider_output(self, response, result, spider):
# Called with the results returned from the Spider, after
# it has processed the response.
# Must return an iterable of Request, or item objects.
for i in result:
yield i
def process_spider_exception(self, response, exception, spider):
# Called when a spider or process_spider_input() method
# (from other spider middleware) raises an exception.
# Should return either None or an iterable of Request or item objects.
pass
async def process_start(self, start):
# Called with an async iterator over the spider start() method or the
# matching method of an earlier spider middleware.
async for item_or_request in start:
yield item_or_request
def spider_opened(self, spider):
spider.logger.info("Spider opened: %s" % spider.name)
class ScraperDownloaderMiddleware:
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the downloader middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_request(self, request, spider):
# Called for each request that goes through the downloader
# middleware.
# Must either:
# - return None: continue processing this request
# - or return a Response object
# - or return a Request object
# - or raise IgnoreRequest: process_exception() methods of
# installed downloader middleware will be called
return None
def process_response(self, request, response, spider):
# Called with the response returned from the downloader.
# Must either;
# - return a Response object
# - return a Request object
# - or raise IgnoreRequest
return response
def process_exception(self, request, exception, spider):
# Called when a download handler or a process_request()
# (from other downloader middleware) raises an exception.
# Must either:
# - return None: continue processing this exception
# - return a Response object: stops process_exception() chain
# - return a Request object: stops process_exception() chain
pass
def spider_opened(self, spider):
spider.logger.info("Spider opened: %s" % spider.name)

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# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html
# useful for handling different item types with a single interface
from itemadapter import ItemAdapter
class ScraperPipeline:
def process_item(self, item, spider):
return item

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# Scrapy settings for scraper project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
# https://docs.scrapy.org/en/latest/topics/settings.html
# https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
# https://docs.scrapy.org/en/latest/topics/spider-middleware.html
BOT_NAME = "scraper"
SPIDER_MODULES = ["scraper.spiders"]
NEWSPIDER_MODULE = "scraper.spiders"
ADDONS = {}
# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = "scraper (+http://www.yourdomain.com)"
# Obey robots.txt rules
ROBOTSTXT_OBEY = True
# Concurrency and throttling settings
#CONCURRENT_REQUESTS = 16
CONCURRENT_REQUESTS_PER_DOMAIN = 1
DOWNLOAD_DELAY = 1
# Disable cookies (enabled by default)
#COOKIES_ENABLED = False
# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False
# Override the default request headers:
#DEFAULT_REQUEST_HEADERS = {
# "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
# "Accept-Language": "en",
#}
# Enable or disable spider middlewares
# See https://docs.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
# "scraper.middlewares.ScraperSpiderMiddleware": 543,
#}
# Enable or disable downloader middlewares
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
# "scraper.middlewares.ScraperDownloaderMiddleware": 543,
#}
# Enable or disable extensions
# See https://docs.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
# "scrapy.extensions.telnet.TelnetConsole": None,
#}
# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
#ITEM_PIPELINES = {
# "scraper.pipelines.ScraperPipeline": 300,
#}
# Enable and configure the AutoThrottle extension (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/autothrottle.html
#AUTOTHROTTLE_ENABLED = True
# The initial download delay
#AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
#AUTOTHROTTLE_MAX_DELAY = 60
# The average number of requests Scrapy should be sending in parallel to
# each remote server
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# Enable showing throttling stats for every response received:
#AUTOTHROTTLE_DEBUG = False
# Enable and configure HTTP caching (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = "httpcache"
#HTTPCACHE_IGNORE_HTTP_CODES = []
#HTTPCACHE_STORAGE = "scrapy.extensions.httpcache.FilesystemCacheStorage"
# Set settings whose default value is deprecated to a future-proof value
FEED_EXPORT_ENCODING = "utf-8"

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# This package will contain the spiders of your Scrapy project
#
# Please refer to the documentation for information on how to create and manage
# your spiders.

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# Automatically created by: scrapy startproject
#
# For more information about the [deploy] section see:
# https://scrapyd.readthedocs.io/en/latest/deploy.html
[settings]
default = scraper.settings
[deploy]
#url = http://localhost:6800/
project = scraper