Python Script to Read and Judge 1,500 Legal Cases

If you've ever dealt with public-sector data, you know the pain. It's often locked away in the most user-unfriendly format imaginable: the PDF.

\ I recently found myself facing a mountain of these. Specifically, hundreds of special education due process hearing decisions from the Texas Education Agency. Each document was a dense, multi-page legal decision. My goal was simple: figure out who won each case—the "Petitioner" (usually the parent) or the "Respondent" (the school district).

\ Reading them all manually would have taken weeks. The data was there, but it was unstructured, inconsistent, and buried in legalese. I knew I could automate this. What started as a simple script evolved into a full-fledged data engineering and NLP pipeline that can process a decade's worth of legal decisions in minutes.

\ Here's how I did it.

The Game Plan: An ETL Pipeline for Legal Text

ETL (Extract, Transform, Load) is usually for databases, but the concept fits perfectly here:

1. Extract: Build a web scraper to systematically download every PDF decision from the government website and rip the raw text out of it.
2. Transform: This is the magic. Build an NLP engine that can read the unstructured text, understand the context, and classify the outcome of the case.
3. Load: Save the results into a clean, structured CSV file for easy analysis.

Step 1: The Extraction - Conquering the PDF Mountain

First, I needed the data. The TEA website hosts decisions on yearly pages, so the first script, texasdueprocess_extract.py, had to be a resilient scraper. I used a classic Python scraping stack:

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• requests and BeautifulSoup4 to parse the HTML of the index pages and find all the links to the PDF files.
• PyPDF2 to handle the PDFs themselves.

\ A key insight came early: the most important parts of these documents are always at the end—the "Conclusions of Law" and the "Orders." Scraping the full 50-page text for every document would be slow and introduce a lot of noise. So, I optimized the scraper to only extract text from the last two pages.

\ texasdueprocess_extract.py - Snippet

# A look inside the PDF extraction logic import requests import PyPDF2 import io  def extract_text_from_pdf(url):     try:         response = requests.get(url)         pdf_file = io.BytesIO(response.content)         pdf_reader = PyPDF2.PdfReader(pdf_file)          text = ""         # Only process the last two pages to get the juicy details         for page_num in range(len(pdf_reader.pages))[-2:]:             page = pdf_reader.pages[page_num]             text += page.extract_text()         return text     except Exception as e:         print(f"Error processing {url}: {e}")         return None 

This simple optimization made the extraction process much faster and more focused. The script iterated through years of decisions, saving the extracted text into a clean JSON file, ready for analysis.

Step 2: The Transformation - Building a Legal "Brain"

This was the most challenging and interesting part. How do you teach a script to read and understand legal arguments?

\ My first attempt (examineeddata.py) was naive. I used NLTK to perform n-gram frequency analysis, hoping to find common phrases. It was interesting but ultimately useless. "Hearing officer" was a common phrase, but it told me nothing about who won.

\ I needed rules. I needed a domain-specific classifier. This led to the final script, examineeddata_2.py, which is built on a few key principles.

A. Isolate the Signal with Regex

Just like in the scraper, I knew the "Conclusions of Law" and "Orders" sections were the most important. I used a robust regular expression to isolate these specific sections from the full text.

\ examineeddata_2.py - Regex for Sectional Analysis

# This regex looks for "conclusion(s) of law" and captures everything # until it sees "order(s)", "relief", or another section heading. conclusions_match = re.search(     r"(?:conclusion(?:s)?\s+of\s+law)(.+?)(?:order(?:s)?|relief|remedies|viii?|ix|\bbased upon\b)",     text, re.DOTALL | re.IGNORECASE | re.VERBOSE)  # This one captures everything from "order(s)" or "relief" to the end of the doc. orders_match = re.search(     r"(?:order(?:s)?|relief|remedies)(.+)$",     text, re.DOTALL | re.IGNORECASE | re.VERBOSE )  conclusions = conclusions_match.group(1).strip() if conclusions_match else "" orders = orders_match.group(1).strip() if orders_match else "" 

This allowed me to analyze the most decisive parts of the text separately and even apply different weights to them later.

B. Curated Keywords and Stemming

Next, I created two lists of keywords and phrases that strongly indicated a win for either the Petitioner or the Respondent. This required some domain knowledge.

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• Petitioner Wins: "relief requested…granted", "respondent failed", "order to reimburse"
• Respondent Wins: "petitioner failed", "relief…denied", "dismissed with prejudice"

\ But just matching strings isn't enough. Legal documents use variations of words ("grant", "granted", "granting"). To solve this, I used NLTK's PorterStemmer to reduce every word in both my keyword lists and the document text to its root form.

from nltk.stem import PorterStemmer stemmer = PorterStemmer()  # Now "granted" becomes "grant", "failed" becomes "fail", etc. stemmed_keyword = stemmer.stem("granted") 

This made the matching process far more effective.

C. The Secret Sauce: Negation Handling

This was the biggest "gotcha." Finding the keyword "fail" is great, but the phrase "did not fail to comply" completely flips the meaning. A simple keyword search would get this wrong every time.

\ I built a negation-aware regex that specifically looks for words like "not," "no," or "failed to" appearing before a keyword.

\ examineeddata_2.py - Negation Logic

For each keyword, build a negation-aware regex keyword = "complied" negated_keyword = r"\b(?:not|no|fail(?:ed)?\s+to)\s+" + re.escape(keyword) + r"\b" First, check if the keyword exists if re.search(rf"\b{keyword}\b", text_section): #   THEN, check if it's negated if re.search(negated_keyword, text_section):   # This is actually a point for the OTHER side!   petitioner_score += medium_weight else: # It's a normal, positive match   respondent_score += medium_weight 

This small piece of logic dramatically increased the accuracy of the classifier.

Step 2: The Transformation - Building a Legal "Brain"

Finally, I put it all together in a scoring system. I assigned different weights to keywords and gave matches found in the "Orders" section a 1.5x multiplier, since an order is a definitive action.

\ The script loops through every case file, runs the analysis, and determines a winner: "Petitioner," "Respondent," "Mixed" (if both scored points), or "Unknown." The output is a simple, clean `decision_analysis.csv` file.

| docket | winner | petitioner_score | respondent_score | | :--- | :--- | :--- | :--- | | 001-SE-1023 | Respondent | 1.0 | 7.5 | | 002-SE-1023 | Petitioner | 9.0 | 2.0 | | 003-SE-1023 | Mixed | 3.5 | 4.0 |  A quick `df['winner'].value_counts()` in Pandas gives me the instant summary I was looking for. 

Final Thoughts

This project was a powerful reminder that you don't always need a massive, multi-billion-parameter AI model to solve complex NLP problems. For domain-specific tasks, a well-crafted, rule-based system with clever heuristics can be incredibly effective and efficient. By breaking down the problem—isolating text, handling word variations, and understanding negation, I was able to turn a mountain of messy PDFs into a clean, actionable dataset. \n