News Categorization: Why Tagging News Content Is Essential for Effective Information Management
News content is a crucial component of automated intelligence platforms. With relevant and timely news data, these platforms can uncover emerging trends in markets, industries, and consumer behaviors. They can also use news data to detect potential threats and risks to businesses and brands.
Most intelligence platforms get their news data from news APIs. This article delves into the importance of news categorization and for news APIs and automated platforms.
What is news categorization?
Category tags are metadata used to organize news articles based on their content. News categorization, or the process of tagging or labeling news content to organize it by specific criteria, such as category, topic, or theme. The primary goal of news categorization is to make searching, filtering, and analyzing news content easier and more efficient. By applying tags to news articles (tagging), news APIs can deliver relevant, targeted, and personalized news data to platforms and applications.
The growing importance of tagging news content
Automated platforms like media intelligence, financial intelligence, and risk intelligence rely on timely and relevant news data to provide deeper and more accurate insights. However, these platforms need tagged news content so they can:
- Save resources — Tagging removes the need for resource-heavy manual query construction and maintenance. Without it, platforms depend on complex boolean queries and keyword searches that require constant tuning to improve accuracy. Automated tagging categorizes content at scale, reducing both computational demands and human effort for efficient news filtering.
- Understand context — Tags enable platforms to understand the context of news stories by linking them to specific topics, entities, themes, and events. Contextual intelligence empowers users to generate more relevant and actionable insights, transforming raw data into a clearer picture of trends, risks, and opportunities.
- Filter data more efficiently — Tags make it easier for platforms to filter through massive volumes of information. With a more straightforward data management process, users can generate and refine insights from news content more efficiently, enhancing both speed and accuracy.
- Enable real-time monitoring — When platforms have access to real-time tagged news data, they can automatically track emerging market trends, consumer behavior changes, and geopolitical events that could impact businesses.
- Personalize insights and recommendations — Tags allow platforms to personalize insights and recommendations based on user-defined interests, enabling users to access the data they need for more actionable and relevant insights.
Tagging also improves AI model training. Well-tagged news data reduces biases in training data because it makes it easier for the model to detect underrepresented topics, regions, or entities. Tagging also provides accurate labels, ensuring models learn from correctly categorized news data.
Now that we know how news content tagging benefits automated platforms, let’s delve into common approaches and techniques for news categorization.
Common approaches to news categorization
Companies that need to manage information, especially massive volumes of news content, need an efficient and accurate approach. The three main approaches to news categorization are:
Manual categorization
Humans manually assign tags to news articles based on the content and context. This method is time consuming and prone to human error, especially when dealing with large volumes of news content. A purely manual news categorization process is not scalable, so most news API providers use an automated or hybrid approach.
Automated categorization
Typically involves using advanced technologies, such as generative AI models, machine learning and natural language processing (NLP) to automate the article and content tagging process. Some tagging systems also use probabilistic models. Automated approaches can handle massive volumes of news content, adapt quickly to new content, and ensure high accuracy.
Hybrid categorization
Most companies that need to categorize news content use a hybrid approach that combines automated processes with human oversight. A hybrid approach works especially well for edge cases and nuanced or high-impact news stories. It also increases news categorization accuracy and flexibility.
Choosing the right approach and techniques is critical to successful news categorization because it directly impacts accuracy, relevance, and speed.
News categorization techniques
There are many techniques for categorizing news. Most news API providers use a combination of techniques which can include:
- Classification models — A machine learning technique where supervised learning algorithms are trained on a dataset of labeled news articles. Each article is associated with a known category. The algorithms are trained to categorize news into predefined classes — e.g., technology, health, business — allowing the model to predict the categories for new articles. Common supervised learning algorithms for classification models include support vector machines (SVM), decision trees, Naïve Bayes, and Random Forests.
- Topic modeling — A type of modeling that uses statistical or probabilistic techniques to identify topics or themes within a collection of documents (news articles). One probabilistic technique is Latent Dirichlet Allocation (LDA), which groups words into “topics” based on word frequency and co-occurrence. Each news article is assigned a category based on the dominant topic it contains. For example, a topic model might identify the topic “artificial intelligence” in articles discussing deep learning, large language models, neural networks, and computer vision.
- Named Entity Recognition (NER) — NER involves identifying, classifying, and extracting named entities in text. Named entities are words or phrases that represent specific items or concepts, such as organizations, products, people, locations, dates, and times. NER can help refine news categories and provide contextual understanding by identifying entity relationships within text. Among the algorithms used for NER are SVM, conditional random fields (CRFs), and Hidden Markov Models (HMMs).
- Sentiment analysis — Also called sentiment classification, sentiment analysis involves using algorithms to analyze text and determine its sentiment or emotional tone. Once an algorithm is trained, a model is saved and used to categorize news articles based on whether they are positive, negative, or neutral. Common algorithms used for sentiment analysis include SVM, Naïve Bayes, and K-Nearest Neighbor.
Some news APIs rely on industry-standardized taxonomies to structure categories, such as International Press Telecommunications Council (IPTC) Media Topics and IAB Content Taxonomy. IPTC Media Topics is a taxonomy that focuses on categorizing text and currently consists of over 1,200 terms. IAB Content Taxonomy provides a “common language” for describing content. Version 3.0 introduced distinct vector labels for “news” and “opinion & op-ed” — they are no longer taxonomically related.
The Role of AI in News Categorization
Nearly all the techniques highlighted above involve the use of AI models. That is largely because machine learning and natural language processing, both subsets of AI, have revolutionized the process of tagging and categorizing news articles and content. Companies that need to categorize news content turn to AI because it:
- Enables automation — With AI, you can automate many of the processes required for news tagging and categorization. AI algorithms can analyze massive volumes of news content, tagging and categorizing thousands, even millions of articles automatically.
- Improves accuracy — AI models can quickly identify entities, entity relationships, textual nuances, and sentiment within text. AI models also recognize themes, tones, complex sentence structures, and idioms. These capabilities make news tagging and categorization highly accurate and relevant.
- Ensures high scalability — News APIs and automated platforms require high scalability, which manual processes can’t accommodate. AI allows news tagging and categorization processes to scale automatically as the amount of news content increases. For example, Webz.io’s News API accesses 3.5M+ news articles daily. Our API can tag and categorize millions of news articles thanks to AI.
- Enables multilingual capabilities — A good news API accesses news content from around the world, providing news data in many different languages. Using AI, news APIs and automated platforms can implement multilingual capabilities. These capabilities allow the API or platform to categorize content across multiple languages, overcoming language barriers.
Thanks to AI, news APIs and automated platforms can significantly speed up news content tagging and categorization while increasing accuracy and relevancy.
Trust your data collection to capture the information you need
How a news API approaches news tagging and categorization is critical for automated platforms. For example, Webz.io’s News API includes IPTC topical categories, entity enrichment, NLP enrichment, sentiment analysis, and multilingual search in 170+ languages. With these features you can get relevant news data that is enriched with smart entities and sentiment. The end result is that your solutions can generate powerful, unique and actionable insights faster and easier.
Ready to get the data you need to make your insights stronger? Talk to one of our data experts today!
