Enhanced News Scraping and Storage Pipeline

• Day: 2024-10-01
• Time: 03:10 to 03:55
• Project: Media
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: News Scraping, Automation, NLP, Debugging, Database

Description

Session Goal

The objective of this session was to enhance a news scraping setup by expanding content extraction capabilities, implementing unstructured data storage with MongoDB, integrating NLP for keyword extraction and classification, and automating the triage of news articles.

Key Activities

• Developed a comprehensive plan for a news processing pipeline, focusing on automation and NLP integration.
• Outlined the implementation plan for a NewsDataCollector bot to scrape news from RSS feeds and store them in a SQLite database.
• Implemented verbose testing for the NewsDataCollector to ensure data integrity and trace execution flow.
• Enhanced a Python function for news collection with verbose print statements for debugging.
• Debugged issues related to database storage, specifically addressing errors in the save_to_db function.
• Revised the save_to_db method to ensure the news table exists before data insertion, preventing operational errors.

Achievements

• Successfully outlined and partially implemented a robust framework for automated news scraping and storage.
• Improved error handling and debugging capabilities in the news data collection process.

Pending Tasks

• Complete the integration of NLP for keyword extraction and classification.
• Finalize the automation of news article triage.
• Conduct further testing to ensure the robustness of the entire pipeline.

Evidence

• source_file=2024-10-01.sessions.jsonl, line_number=1, event_count=0, session_id=44fdd760cda49c1e6d4b881b9c836319c7922d160b84becc7dc831f0bdfd4c49
• event_ids: []