News Feed and RSS Parsing: Aggregation for Media and Analytics – Sources, Deduplication, Classification

Modern media and analytical companies work with enormous volumes of information coming from thousands of sources. Without automated aggregation, it is impossible to track trends, competitors, and public opinion in a timely manner. In this article, we will look at the key aspects of building news aggregation systems: from finding and connecting sources to deduplication and classification of content. We will also show how solutions based on data parsing and cloud platform technologies can become a reliable foundation for your business.

Sources of News Content: RSS, API, and Website Parsing

Aggregation begins with defining sources. Today, three main methods of obtaining news are available: subscribing to RSS/Atom feeds, working through news aggregator APIs, and direct website parsing. The choice depends on requirements for completeness, speed, and legal aspects.

RSS and Atom – The Classics

RSS 2.0 and Atom are the least costly method. Most news outlets, blogs, and corporate portals provide feeds. The parser simply polls the URL at set intervals and extracts titles, descriptions, dates, and links. Pros: simplicity, standardized format, high speed. Cons: limited volume (often only a brief summary), not all resources support RSS.

To connect hundreds of sources, a scalable architecture is needed. This is where our expertise in cloud services helps — automatic scaling to handle peak news load.

News APIs and Aggregators

Services like NewsAPI, GDELT, or EventRegistry provide structured access to millions of articles via REST API. This is a quick way to cover many sources without setting up parsers. However, they have limitations on request frequency, commercial licenses, and data is not always up-to-date. An effective approach is to combine direct RSS feeds with APIs for redundant coverage.

Website Parsing

When a site has no RSS or API, data must be extracted directly from HTML. Modern browser and headless tools (Puppeteer, Playwright) allow emulating user behavior, handling dynamic JavaScript, and bypassing simple blocks. However, parsing requires compliance with laws, respect for robots.txt, and acceptable loads on source servers. The ESK Solutions team has many years of experience in legal parsing of news portals and can develop a specialized data collector for you — more details on the parsing services page.

For media analysis, it is especially important to collect not only news articles but also metadata: authors, topics, tags, publication time. All of this is normalized in a single repository.

News Deduplication: How Not to Be Overwhelmed by Repetitions

When aggregating from many sources, the same news item (an agency reprint or cross-posting) will come in multiple times. Without deduplication, the feed becomes a pile of copies, reducing its value for analytics and overloading storage.

Why Repetitions Are a Problem

Duplicates noise up data, distort mention statistics, and increase storage and processing costs. For a journalist or analyst, it's important to see unique information, not a hundred identical messages.

Methods for Detecting Duplicates

From simple to advanced:

• Exact match by URL or text — comparing MD5 or SHA-1 hashes. Fast, but won't work with minor changes (adding an ad block, spelling corrections).
• Fuzzy comparison at the sentence level — splitting into shingles, calculating Levenshtein distance or Jaccard coefficient. Allows finding nearly identical articles.
• Locality-sensitive hashing (LSH) — an efficient method for large volumes. MinHash or SimHash algorithms group similar documents without pairwise comparison.
• Clustering with embeddings — modern NLP models (BERT, Sentence Transformers) convert text into vectors, after which semantically similar news items are grouped. This helps even when the text is rewritten in one's own words.

Practice shows a combination: a fast filter for exact duplicates, followed by fuzzy deduplication at a chosen similarity threshold. It's also important to account for time frames — a repeat of news from a week ago may no longer be a duplicate but an update or new context.

To implement such logic, a well-designed server side is required, and here our team of SaaS applications development can help. We create turnkey aggregators with smart deduplication.

News Classification: Structuring Information

The collected data array must be categorized: broken down by topic, tagged with companies and people, and assessed for sentiment. This turns the raw stream into a tool for analytics.

Why Classification Is Needed

Without structure, search and filtering become a nightmare. Media monitoring requires automatically assigning an article to a category like 'politics', 'technology', 'finance', etc. Investment banks and marketing agencies want to extract only specific events: mergers, resignations, product launches. Sentiment analysis allows monitoring brand reputation.

Classification Approaches: Rules and Machine Learning

Two main paths:

• Rule-based systems: a set of keywords, regular expressions, and Boolean conditions. Quick to implement, but does not scale and requires constant rule updates.
• Machine learning: models based on transformers (BERT, RoBERTa) or lighter classifiers (FastText) are trained on labeled datasets and then accurately classify new texts. An ML model can grasp context and semantics, dramatically improving quality.

We recommend a hybrid approach: rules for clear cases and ML for borderline ones. Additionally, you can extract named entities (NER) — companies, people, geo-objects — and enrich article metadata.

Implementing a classifier requires expertise in NLP and data engineering. ESK Solutions develops classification modules as part of SaaS aggregators — contact us for a consultation on the SaaS development page.

Topic Categorization and Sentiment Analysis

Topic categorization can be built on either a tree taxonomy or dynamic clusters. Sentiment analysis determines emotional coloring (positive, neutral, negative). For businesses, this allows monitoring the tone of mentions in real time and responding quickly to crises.

Aggregation System Architecture: What to Pay Attention To

Building an aggregator is not just a parser script. A comprehensive solution is needed: from data ingestion to user interface.

Data Collection and Normalization

Streams from different sources must be unified into a single format: a JSON structure with fields like title, content, pub_date, source_url, authors, tags, sentiment, etc. Adapters are written for each source type. A microservice architecture allows adding new connectors without stopping the system.

Storage and Search

News is naturally stored in a document-oriented database (Elasticsearch, MongoDB). Elasticsearch provides full-text search, filtering by date and tags, and aggregations. Columnar stores (ClickHouse) can be connected for analytical queries.

Scaling with Cloud Services

System load is often uneven: there can be a surge of publications in the morning and evening. Cloud services allow flexible scaling of capacity during peak hours and savings during idle times. Using message queues (Kafka, RabbitMQ) ensures reliable delivery and processing without losses. Our specialists will design and deploy the entire infrastructure as part of our cloud services development service.

For the end user (journalist, analyst), a web interface needs to be created: a panel with a news feed, filters, export to Excel or PDF. Here our experience in web development comes in handy — from responsive design to complex dashboards.

ESK Solutions: Why Us

The custom development studio ESK Solutions delivers full-cycle projects — from requirements gathering to deployment and support. We combine technical competencies in parsing, cloud computing, and machine learning so that your news aggregation system is reliable, scalable, and accurate.

By contacting us, you will get:

• Audit of sources and architecture of the future aggregator
• Development and configuration of parsers for RSS, API, and websites
• Implementation of deduplication and classification based on ML
• Cloud deployment and 24/7 monitoring
• Integration with existing CRM and BI systems

Learn more about cases and offered services on the respective pages: website and data parsing, SaaS solutions development, cloud infrastructure, web development, CRM systems.

Frequently Asked Questions

Can news be automatically collected from any websites?

Most news resources have RSS or API. In the absence of open interfaces, HTML page parsing is used, but it requires compliance with legislation and robots.txt rules. ESK Solutions offers legal parsing services adapted to client requirements. See more about parsing services.

How often should data be updated in a news aggregator?

For operational monitoring, updates should occur every 5–15 minutes. Frequency depends on sources and infrastructure load. Our specialists help configure the optimal mode using cloud solutions — visit the cloud services page.

How difficult is it to implement machine learning for news classification?

With labeled data and proper expertise, this is achievable within the development of a specialized SaaS aggregator. We can create ML models for categorization, entity extraction, and sentiment analysis. Learn more about SaaS application development.

Can news feeds be integrated into corporate systems, such as CRM?

Yes, aggregated and labeled data can be sent to a CRM system to enrich customer profiles or to analytical dashboards. ESK develops turnkey integrations, including the CRM development area.