vath/docs/tasks.org

• [X] t1.1: scrape one forum (1)
  • acceptance criteria
  • notes
  • evidence
• [X] t1.2: initial 4o sentiment
  • acceptance criteria
  • notes
  • evidence
• [ ] t1.2.1: 4o with batch processing
  • acceptance criteria
  • notes
  • evidence
• [ ] X: complete proposal information
  • acceptance criteria

[X] 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. ViewComments (townhall.virginia.gov/L/ViewComments.cfm?CommentID=#) appears to be raw list of all comments on forum - could be useful later for whole-scrape Append forum id to viewall per forum (townhall.virginia.gov/L/ViewComments.cfm?GdocForumID=452) Comments are hydrated in backend via js-cued button (AJAX?).

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
3. store forum as distinct item with title, desc
4. add forum ID in comment filename, eg forum452_comments_<datetime>.jsonl
5. remove reg_title and reg_desc from each comment; these belong in forum item
6. parse datetimes into object for later use (plotting)

notes

• scraper/spiders/forum.py — ForumSpider using ViewComments.cfm?GdocForumID=N with POST pagination. First request fetches page 1 (vPerPage=500), discovers the last page number from the form's link, generates all remaining page requests upfront. Parses each div.Cbox for all required fields.
• scraper/items.py — CommentItem with forum_id, reg_title, reg_desc, comment_id, author, date, title, text
• tests/test_forum_spider.py — 7 tests, all passing
• Settings: DEFAULT_RESPONSE_ENCODING=utf-8 (fixes Windows-1251 meta-tag mismatch), HTTPCACHE_ENABLED=True, feed output to output/
• ViewComments.cfm instead of comments.cfm: POST to Comments.cfm returned a 500 error (wrong endpoint). ViewComments.cfm?GdocForumID=N is the correct listing URL, returns full comment text on the page itself — no per-comment follow requests needed.
• Span-wrapped text: .divComment p::text missed 3.6% of comments where text is in <p><span>text</span></p>. Fixed to .divComment *::text, .divComment::text. Worth knowing for when the spider is extended to other forums.
• start() vs start_requests(): Scrapy 2.13+ deprecates start_requests() in favor of async def start()
• ForumItem vs CommentItem: ForumItem (forum_id, reg_title, reg_desc) yielded once on first page; CommentItem no longer carries reg_title/reg_desc. Both land in the same JSONL feed.
• Dynamic output filename: set via from_crawler() overriding FEEDS at 'spider' priority — format is output/forum{id}_comments_%(time)s.jsonl. FEEDS removed from settings.py; spider owns it.
• Date parsing: _parse_date() normalizes whitespace, upper-cases, parses "%m/%d/%y %I:%M %p" → ISO 8601; falls back to raw string on failure.

evidence

• commit: beb5cf4 (AC1-2), e7df0b2 (AC3-6)

• tests: 8 passing (`python -m pytest tests -q`) or (`python -m pytest tests/`)

  • `scrapy crawl forum -a forum_id=452 -s LOG_LEVEL=WARNING 2>&1`
  • retrieved 9083 comments
• datetime: [2026-05-05 Tue 14:00]

[X] t1.2: initial 4o sentiment

Write a simple manual pipeline for gpt-4o that reads one scraped forum jsonl file and roduces a separate analyzed jsonl file. this step must not mutate scraper output. analysis should classify each comment for regulatory stance, generic tone/sentiment, confidence, and enough rationale/evidence to support later dashboard drilldown. Should be run manually, separate from scraper. You may use scrapy, but are not required to.

• Sentiment is derived, not scraped - keep separate from raw comments.
• keep jsonl as interchange/audit format

acceptance criteria

1. input scraped jsonl doc by filename/path, e.g. "./output/forum452_comments_<datetime>.jsonl"

   • handle mixed itemtypes, e.g., forum + comment items

2. output new analysis file, e.g., "analysis/forum452_<datetime>_<model>_<datetime>.jsonl"

   • one analysis record per comment
   • include run_id, forum_id, comment_id, analyzed_at, model, prompt_version

3. capture stance toward proposed reg/guidance:

   • `stance`: support, oppose, neutral, unknown
   • `confidence`: 0-1
   • short rationale, if provided by model
4. capture generic sentiment/tone separately from stance: `tone`=positive, negative, neutral, mixed, unclear
5. capture issue/topic tags for later grouping, may be empty
6. use .env for api key management
7. document the exact prompt version used; prompt text may live in code or docs, but must have a version string/hash in output records
8. for this run, an option to run the first N comments (5, 10, 20, 50) - will add batch processing later

notes

• analysis/gpt4o/analysis.py: standalone script; core functions importable for tests.
• Prompt version = SHA-256[:7] of SYSTEM_PROMPT+USER_TEMPLATE; auto-updates on prompt change.
• Output: analysis/gpt4o/forum{id}_{scrape_tsmodelrun_ts}.jsonl, one record per comment.
• –limit {5,10,20,50} for test runs; omit for full corpus. Batch processing planned for later.
• Incremental flush after each record: safe to interrupt and inspect partial output.
• temperature=0.0 for deterministic, reproducible classifications across runs.
• Retry: 3 attempts (delays 1s, 2s) on RateLimitError; all other exceptions → error record + continue.
• openai==2.34.0 installed; python-dotenv already present; key loaded from .env via OPENAI_API_KEY.
• MAX_COMMENT_CHARS=6000: covers >99% without truncation; outliers (e.g. 18k-char law firm brief) flagged with truncated=True.

evidence

• commit: d834d18

• tests: 20 passing (pytest tests/test_gpt4o_analysis.py), 28 total across suite

  • `python ./analysis/gpt4o/analysis.py –limit 5 ./output/f452.jsonl`
  • see: ./analysis/gpt4o/forum452_unknown_gpt-4o_2026-05-05T18-48-32+00-00.jsonl
• date: [2026-05-05 Tue 15:00]

[ ] t1.2.1: 4o with batch processing

acceptance criteria

1. input scraped jsonl doc by filename/path, and process the whole thing via batch processing

notes

evidence

• commit:
• tests:
• date:

[ ] X: complete proposal information

Ensure we capture as much useful information as possible about the actual proposal - contact information, etc. what the state actually says about what was posted.

acceptance criteria

1. Item: `Forum` stores id, url, proposal title, description, open/close date, number of comments, agency, board, guidance document id

   • add details for guidanceDoc, publication date, comments, guidance docs - eg: https://www.townhall.virginia.gov/L/GDocForum.cfm?GDocForumID=452
2. Item: `Comment` stores forum_id, comment_id, author, title, text, date, url