📅 2024-06-09 — Session: Automated News Article Processing Pipeline Completion
🕒 16:30–18:30
🏷️ Labels: News Processing, Automation, NLP, BERT, Data Extraction
📂 Project: Media
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to finalize the development of an automated pipeline for processing news articles, including scraping, extraction, classification, and analysis.
Key Activities
- Reviewed techniques for information extraction and summarization, focusing on entity recognition and data processing.
- Planned and executed workflows for web scraping using Newspaper3k and database setup for storing extracted data.
- Implemented initial news scraping and extraction workflows in Python, with future NLP tasks planned.
- Analyzed article titles related to Argentine politics, categorizing them into key themes.
- Developed a classification system for news articles using BERT, with storage in BigQuery.
- Addressed Python library warnings and import errors related to BERT model deployment.
- Managed disk space using Linux command line tools.
- Fine-tuned BERT for sequence classification.
Achievements
- Successfully set up an automated pipeline for news article processing.
- Completed article fetching, content extraction, and classification.
Pending Tasks
- Future enhancements include implementing entity recognition and summarization techniques.
Future Steps
- Continue refining the classification system and explore additional NLP tasks.
- Optimize media strategies and AI project objectives using structured checklists.