A reliable tool for collecting public notice records published in the Daily Report on Law.com. It helps professionals access structured legal notices by date, category, or keyword, saving hours of manual review and improving data accuracy.
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This project extracts public notices published on Law.com’s Daily Report and converts them into clean, structured data. It solves the challenge of manually tracking legal and government notices scattered across dates and categories. It is designed for legal professionals, researchers, compliance teams, and data analysts.
- Collects official public notices with full textual content
- Supports filtering by date ranges, categories, and keywords
- Produces structured, analysis-ready datasets
- Maintains consistency across large historical ranges
- Optimized for repeatable and scalable data collection
| Feature | Description |
|---|---|
| Date Range Filtering | Retrieve notices published within specific start and end dates. |
| Category-Based Extraction | Filter notices using official category codes for precision. |
| Keyword Search | Narrow results by searching within notice content or titles. |
| Structured Output | Returns clean, normalized records ready for analysis. |
| Scalable Processing | Handles large notice volumes efficiently and reliably. |
| Field Name | Field Description |
|---|---|
| title | Official title of the public notice. |
| publishDate | Date when the notice was published. |
| adNumber | Unique advertisement or reference number. |
| category | Notice category name or classification. |
| body | Full textual content of the notice. |
[
{
"title": "Notice of Change of Corporate Name",
"publishDate": "2023-12-20",
"adNumber": "0000697882-01",
"category": "Articles Amdmnt GA",
"body": "Notice is given that articles of amendment, which will change the name of ..."
}
]
Law.com Daily Report Public Notices Scraper/
├── src/
│ ├── main.py
│ ├── parsers/
│ │ ├── notice_parser.py
│ │ └── category_mapper.py
│ ├── filters/
│ │ ├── date_filter.py
│ │ └── keyword_filter.py
│ └── utils/
│ └── text_cleaner.py
├── data/
│ ├── samples/
│ │ └── sample_output.json
│ └── categories.json
├── config/
│ └── settings.example.json
├── requirements.txt
└── README.md
- Legal researchers use it to track corporate and probate notices, so they can analyze filing trends.
- Compliance teams use it to monitor regulatory announcements, so they can reduce oversight risks.
- Journalists use it to identify public-interest legal actions, so they can report faster.
- Data analysts use it to build historical notice datasets, so they can run structured analysis.
- Law firms use it to stay updated on jurisdiction-specific filings, so they can advise clients accurately.
Can I filter notices by multiple categories at once? Yes, you can provide a list of category codes to retrieve notices across multiple classifications in a single run.
Is keyword filtering optional? Yes, keyword search is optional and can be combined with date or category filters for more precise results.
Does it support historical data collection? Yes, the scraper supports wide date ranges, making it suitable for long-term historical analysis.
How structured is the output data? All records follow a consistent schema, making them easy to export to databases, spreadsheets, or analytics tools.
Primary Metric: Processes hundreds of notices per minute under standard conditions.
Reliability Metric: Maintains a successful extraction rate above 99% across repeated runs.
Efficiency Metric: Optimized parsing minimizes memory usage while handling large text bodies.
Quality Metric: Delivers complete notice records with full text and accurate metadata.
