Extracting Intelligence from RSS News Feeds Using Python and AI: From Global Headlines to Actionable Intelligence - Brossura

Hosmer, Chet

Brossura

ISBN 13: 9798868827723

Casa editrice: APRESS L.P., 2026

Riassunto

In a world flooded with digital information, the ability to automatically extract meaningful and actionable insights from global news feeds is a critical skill. Extracting Actionable Information from RSS Feeds Using Python and AI offers a hands-on guide for leveraging Python and OpenAI to transform raw RSS content―both in English and non-English languages―into structured, insightful data.

This book walks readers through building intelligent pipelines that go beyond simple feed parsing. Using advanced natural language processing and AI techniques, readers will learn how to extract vital elements from each news article, including:

• Author identification
• Detailed, AI-generated summaries
• Assessment of global, political, and social relevance
• Detection of potential threats or risks
• Named entity recognition (people, places, organizations)

Whether you're building real-time threat intelligence systems, media monitoring dashboards, or conducting geopolitical analysis, this book equips you with the tools and source code to accelerate your development. Each chapter includes fully functional Python scripts that can be immediately applied or extended to meet specific needs.

Designed for developers, analysts, and technologists, this practical and forward-looking book bridges the gap between unstructured content and actionable intelligence―at the speed of the global news cycle.

What You’ll Learn:

• Understand how to collect and process RSS feed data from both English and non-English sources using Python.
• Apply OpenAI-powered natural language processing to extract key elements such as author, summary, relevance, and threat indicators from news articles.
• Perform named entity recognition (NER) to identify and extract people, places, and organizations mentioned in each article.
• Evaluate the geopolitical, social, and political relevance of news stories using AI-driven content analysis techniques.
• Utilize and customize the provided Python source code to build or enhance real-time content extraction and analysis tools.

Who This Book Is for:

Primary Target Readers include:

• Data Analysts and Intelligence Professionals: Professionals in government, cybersecurity, media monitoring, or corporate intelligence who need to extract and act on relevant news information in real time.
• Python Developers and AI Enthusiasts: Intermediate to advanced Python programmers looking to integrate AI for real-world content analysis, especially those interested in OpenAI and natural language processing.
• Journalists and Media Researchers: Those seeking to automate content curation, perform author attribution, or assess bias and relevance across diverse news sources globally.
• Academics and Students in Data Science, AI, or Digital Humanities: Educators and learners looking for applied projects in NLP, multilingual processing, and AI-driven analysis.
• Tech-Savvy Policy Makers and Think Tank Researchers: Readers who monitor emerging global narratives and want automated tools to help assess political, social, and security implications.

Su questo libro

Informazioni sull'autore

Chet Hosmer is the founder of Python Forensics, a Non-Profit Organization that provides research and python scripts to help with advanced investigative challenges. Chet also serves as a Designated Campus Colleague at the University of Arizona.

Chet has made numerous appearances to discuss emerging cyber threats including NPR, ABC News, Forbes, IEEE, The New York Times, The Washington Post, Government Computer News, Salon.com and Wired Magazine. He has seven published books with Apress and Elsevier that focus on Python Forensics, data hiding, passive network defense strategies, PowerShell, and IoT. In addition, Chet presents at major conferences each year including RSA, TechnoSecurity, HTCIA, Blackhat, and DEFCON.

Dalla quarta di copertina

In a world flooded with digital information, the ability to automatically extract meaningful and actionable insights from global news feeds is a critical skill. Extracting Actionable Information from RSS Feeds Using Python and AI offers a hands-on guide for leveraging Python and OpenAI to transform raw RSS content—both in English and non-English languages—into structured, insightful data.

This book walks readers through building intelligent pipelines that go beyond simple feed parsing. Using advanced natural language processing and AI techniques, readers will learn how to extract vital elements from each news article, including:

• Author identification
• Detailed, AI-generated summaries
• Assessment of global, political, and social relevance
• Detection of potential threats or risks
• Named entity recognition (people, places, organizations)

Whether you're building real-time threat intelligence systems, media monitoring dashboards, or conducting geopolitical analysis, this book equips you with the tools and source code to accelerate your development. Each chapter includes fully functional Python scripts that can be immediately applied or extended to meet specific needs.

Designed for developers, analysts, and technologists, this practical and forward-looking book bridges the gap between unstructured content and actionable intelligence—at the speed of the global news cycle.

What You’ll Learn:

• Understand how to collect and process RSS feed data from both English and non-English sources using Python.
• Apply OpenAI-powered natural language processing to extract key elements such as author, summary, relevance, and threat indicators from news articles.
• Perform named entity recognition (NER) to identify and extract people, places, and organizations mentioned in each article.
• Evaluate the geopolitical, social, and political relevance of news stories using AI-driven content analysis techniques.
• Utilize and customize the provided Python source code to build or enhance real-time content extraction and analysis tools.

Dettagli dell'edizione

• Editore: APRESS L.P.
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798868827723
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 196
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania