📅 2025-05-26 — Session: Developed unified text cleaning function for web content
🕒 02:00–02:25
🏷️ Labels: Web Scraping, Text Processing, Python, Data Cleaning
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to analyze and process web content from various institutional pages, focusing on cleaning and structuring the data for further analysis.
Key Activities
- Analyzed the ICC Institutional Page to identify key topics and named entities, recommending post-processing steps for content normalization.
- Developed a Python function using regular expressions to remove URLs from text, enhancing the cleanliness of crawled content.
- Conducted a structured analysis of the UBA Exactas page scrape, identifying key sections and potential data processing actions.
- Addressed duplicate entry handling in document collections using Python, optimizing code to ensure unique entries by path.
- Created a unified Python function,
clean_spider_text, to efficiently clean web-scraped markdown content by removing images, links, and standalone URLs, and normalizing whitespace. - Outlined a structured list of ICC academic publications, providing insights on themes, authors, and publication years.
- Detailed the structure and content of a news aggregation page for ICC, suggesting data extraction and analysis methods.
Achievements
- Successfully developed a unified text cleaning function that combines multiple logic steps into a single implementation, improving efficiency in processing web-scraped content.
- Enhanced understanding of web content structure and data extraction methods for institutional pages.
Pending Tasks
- Implement the recommended post-processing steps for the ICC Institutional Page content.
- Further refine the data extraction methods for the news aggregation page to improve metadata accuracy.