Developed unified text cleaning function for web content

2 min read

Developed unified text cleaning function for web content

  • Day: 2025-05-26
  • Time: 02:00 to 02:25
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Web Scraping, Text Processing, Python, Data Cleaning

Description

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.

Evidence

  • source_file=2025-05-26.sessions.jsonl, line_number=2, event_count=0, session_id=9da9fb49d369b355adb734a51b48ed2d0737d2c583a4ee10621a855a5f690df2
  • event_ids: []

Graph View

Backlinks