What Entity Extraction Is, and Where It Fits in SEO

Entity extraction is, to my mind, one of the most underused techniques in SEO, and one of the most important to understand, because it is close to how machines actually read text. Search engines and large language models do not see your content as a bag of keywords. They see entities, the real-world things your content is about, and the relationships between them. Google has organised knowledge around entities since it launched its Knowledge Graph in 2012, and that entity-first way of understanding text is now the foundation of how AI search decides what you are relevant for. Learn to see text the way these systems do and you can build much better strategies. This post covers what entity extraction is, where it sits in the wider world of language understanding, how entities differ from keywords, the EAV model, how the models are built, a no-code way to run your first analysis in Google Sheets, a proper tour of where all of it fits in SEO, and ten practical projects to put the data to work (folded in from my guide to practical entity analysis, first published in 2024 and refreshed for 2026).

What entity extraction (NER) is

Entity extraction, also called named entity recognition or entity chunking, is a natural language processing technique that identifies and classifies key pieces of text, the named entities, into predefined categories like people, locations, organisations, events and dates. It takes a string written for human readability, a sentence, a paragraph, a page, a PDF, and adds structure and semantic information a machine can use. You can also custom-train it to recognise niche categories for a specialised domain. That structuring step is why NER is such a common preprocessing stage, including inside the large language models generating answers today.

In machine learning terms, NER is supervised and prediction-based: it is trained on labelled examples, then predicts which of its trained entity categories each span in new text belongs to, weighing context, surrounding words and other entities in the sentence to decide. It can run on any kind of text, social posts, legal documents, scientific papers, and any length, from a single query to a full page.

Where entity extraction sits: semantic analysis

It helps to know the neighbourhood NER lives in, because it explains why the same APIs do so many related jobs.

Think of taking high-level human language and passing it through a system, a compiler, to turn it into something a machine can navigate. Entity analysis falls into the phase of that process called semantic analysis, which is concerned with the meaning of a statement, and which also houses topic modelling and sentiment analysis. Semantic analysis uncovers the definitions of words, phrases and sentences, checks whether they are organised in a way that makes sense, and maps the logic of the relationships between entities. This is why most pre-trained NLP APIs (Google Cloud’s, Amazon’s, IBM’s) bundle entity extraction together with sentiment, entity sentiment, content moderation, syntax analysis and classification, they are all facets of understanding meaning. The practical upshot: besides a list of entities, you get entity type, salience (importance in the document), sentiment, metadata (whether the entity is in the knowledge graph, its attributes), mention counts and the different phrasings used. That extra data is what turns “here are some entities” into real analysis.

If you want the fuller picture, entity analysis is one of several phases in how a system moves from raw human language to machine understanding, a process modelled on compiler design in software engineering. Before meaning can be extracted, a syntactic parser establishes grammatical structure, then the system checks for logic and semantic coherence in how the words relate to one another. Semantic analysis, where entity extraction lives alongside topic modelling and sentiment analysis, is the phase that uncovers what the words actually mean and whether their arrangement makes sense. You never implement any of this yourself, the pre-trained APIs handle the whole pipeline, but knowing that entity extraction is one instrument in a larger orchestra of language understanding helps you appreciate why the data you get back is so rich, and why combining entities with sentiment and topics is so much more powerful than any one of them alone.

Entities are not keywords

This distinction trips up a lot of SEOs, so it is worth nailing.

Keywords are specific words or phrases we value for SEO reasons; entities are distinct, well-defined concepts that exist in the real world, a person, a place, a thing, an idea, each with an entity and an entity type (Barack Obama is a person, the Louvre is a place, a smartphone is a thing or product). Take the word “cone”, it could mean three completely different things depending on context, and only entity recognition resolves that. In a query like “shop online Nike Air Force One”, an SEO reads transactional and navigational intent from the words, while entity extraction identifies Nike (organisation) and Air Force One (product). Sometimes keywords and entities overlap; often they do not. Niche terms like “beginner”, “SEO research” or “guide” get recognised as entities but classified as “other” by general pre-trained models, simply because they are not prominent in the training data.

The EAV model: entities, attributes, variables

Entities get much more powerful once you add structure to them, and this is how they are defined in a knowledge base.

Every entity type has attributes (characteristics or properties), and every attribute has variables (the specific values). Take “dog” as an entity: an attribute is “breed”, and its variables are Labrador, Husky and so on; another attribute is “fur colour”, with its own variables. This EAV model is the backbone of programmatic SEO and information-gain analysis. Take “influencer” as an entity, add attributes like location and family status, and combine variables into “influencers in London”, or multi-variable combinations like “influencers married with kids in the beauty niche”. Each combination is a tailored, finite landing-page target. We go deep on EAV, and how it drives programmatic keyword research, in the Semantic Keyword Research course.

Why use entity analysis in digital marketing and SEO projects

Entity analysis is an essential tool for people trying to improve their organic search performance and aiming to deepen their understanding of how specific entities like brands, people, locations, or products are referenced and connected across digital content. By moving beyond surface-level metrics such as keyword count or keyword density, entity analysis offers a comprehensive view of how these entities are interlinked within the web’s vast content landscape.

Through the examination of how entities are mentioned in blog posts, news articles, and online discussions, search marketers can develop a detailed understanding of the web’s narrative around these entities. This understanding can help craft more effective strategies to improve a website’s organic positioning in search results, identify gaps or opportunities in the market, and foresee shifts in industry trends.

For SEO, entity analysis is crucial in deciphering the relevance and relationships of entities within search queries and content. It allows for creating content that is rich in relevant entities and structured in a way that aligns with how search engines understand and connect these entities. This strategic alignment not only boosts content visibility but also enhances the overall relevance and authority of a website, leading to improved search rankings and user engagement. And with AI search assembling answers from entities and their relationships (more on that below), the same alignment is what makes your content easy for those systems to pick up and cite.

Despite all of that, entity analysis is a machine learning method that is frequently talked about, but still not widely used in day-to-day SEO work. The rest of this guide is about changing that: first how the models work, then how to run your first analysis without writing any code, and then ten practical projects to put the data to work.

How entity extraction models are built

If you ever consider training your own, the process starts with a representative, human-annotated document collection, which is put through feature extraction, encoding and training to produce a classifier (a support vector machine, or any of the classifiers covered in what machine learning classification is) that does the labelling. There are four broad approaches, and knowing their trade-offs tells you why you probably should not start there.

Rule-based is easy to implement and precise for clear patterns, but brittle with ambiguity and language variation, and it needs domain expertise. Classic machine learning trains a classifier on labelled examples. Deep learning and hybrid architectures are what the big providers use, and a hybrid pipeline is more involved than the simple picture: input text runs through segmentation, tokenisation and morphosyntactic analysis (the preprocessing), then an entity-recognition model that may combine classical machine learning, rule-based logic, lexicon-based models and deep learning to extract context and meaning before producing its prediction. These win because the giants have the data and engineering to make them perform. You can custom-train or fine-tune with tools like spaCy, adding contextual word embeddings (which capture meaning in context), conditional random fields (a statistical technique that improves extraction) and active learning (which lets the model pick the most informative examples to label, cutting your labelling effort). But entity extraction is genuinely labour-intensive to build well, so for beginners a pre-trained API like Google Cloud NLP or Amazon Comprehend gets you to a result far faster. Only self-train if you have unique domain entities, strict data-privacy needs (you cannot send data to Google or Amazon), or a real desire to learn the pipeline, and even then, fine-tuning usually beats training from scratch.

If you do weigh up custom training, the factors that decide it are the complexity of the task, how much labelled data you actually have, the computational resources training to a competitive quality would demand, and the accuracy you genuinely need. Weigh those honestly and you will usually conclude that a pre-trained model, or a light fine-tune with spaCy, gets you the great majority of the value for a fraction of the effort, because entity extraction done well needs an enormous, carefully annotated dataset, and the classical approaches on their own rarely match what the giants achieve by combining methods over vast training data.

How to get started with entity analysis in Google Sheets (no code)

Entity analysis with Google’s Natural Language API is a very beginner-friendly way to get started analysing entities from short or long-form content without any coding skills required. The procedure requires obtaining an API key and using a Google Sheets template, originally presented at a Cloud Next event in 2018.

To get started, follow these simple steps:

1. Get your Google Natural Language API key.
2. Copy the Google Sheets template. You can use the MLforSEO entity analysis template, or the original version I demoed, which is built on Google’s own Apps Script sample.

3. Click on the Extensions menu at the top, then Apps Script, then enter your API key in line 21 of the code of the EntitySentiment project. Then click Save project.

4. Return to the sheet. Paste the text you want to analyse the entities from in column B, and its identifier (URL, ID, or any other, depending on the text you want to analyse) in column A.

5. Click on the Sentiment Tools menu and then the button Mark entities and sentiments.

6. Your data will populate the Entity sentiment data worksheet and is ready for visualisation (check out my Data Studio dashboard if you need help with the visualisation of the data).

This process is straightforward and is the same regardless of how you intend to use the data you get from it, or regardless of what type of text you’re extracting the entities from. If you’d rather work in Python, the MLforSEO resources library has a Colab notebook for extracting entities with the Natural Language API, and my SERP analysis notebook shows the same call run over titles and meta descriptions at scale. Now that that’s out of the way, let’s look at what you get back.

With Google’s Natural Language API, as a result of this analysis you get the following:

• Entity: the recognised thing or concept.
• Salience: the importance of the entity in the context in which it is mentioned.
• Sentiment score: the sentiment score associated with the context in which the entity is mentioned.
• Sentiment magnitude: the strength of the sentiment score.
• # of mentions: entity prominence in the analysed text (especially important for longer texts).

A note on API versions (2026). Google released v2 of the Natural Language API in August 2023. It runs on a newer model, but it dropped the salience score and the Wikipedia URL metadata, and it has no entity sentiment method (release notes). Everything in the list above comes from the v1 analyzeEntitySentiment method, which is what these templates use, so stay on v1 for this kind of analysis. If you do switch to v2, the number of mentions is your best stand-in for salience. Either way, the first 5,000 units a month are free per feature, and a unit is 1,000 characters.

Where entity extraction fits in SEO

Once you can extract entities at scale, a lot opens up. And the data you can feed it is everywhere.

You can run entity analysis on long-form text (your content, competitor content, YouTube and podcast transcripts, Reddit posts, PDFs, patents), short-form text (comments, social posts, brand mentions), metadata (titles, URLs, meta descriptions, anchor text for internal and external links) and queries (ranked queries and user queries from Google, YouTube or TikTok). That breadth is what makes it so versatile.

Keyword research gets an entity layer: validate which keywords contain entities, create content maps from closely linked entities, build lists of entity attributes and variables for your writers, and validate which entities sit in the knowledge graph and what their closest associated entities are for linking. Programmatic SEO practically requires it, EAV combinations at scale become your page structure, and doing programmatic work without understanding entities is cumbersome at best. SERP analysis is a big one: use a service like DataForSEO to scrape titles, meta descriptions and URLs for your branded queries, extract the entities, and see which entities appear, how often, whether they sit in the title or the body, and how their sentiment relates to ranking position (with the eternal caveat that correlation is not causation).

Internal linking improves when you identify articles that mention shared entities, link them, and tune anchor text to those entity mentions. Content audits use entity prominence as a lens: flag an entity as prominent if it appears more than a set number of times per page, then check whether your best page on that entity actually covers its attributes in enough depth to compete, whether the entity appears in the title and meta, and whether other articles link to it with the entity in the anchor text, while spotting thin articles with no entities at all as candidates for enhancement, consolidation or sunsetting. And topic maps, the modern way to plan content, are built by grouping entities into clusters; combine entity analysis with EAV query exploration, SERP analysis, competitor analysis and your content audit and you get a genuinely comprehensive map of what to produce to stand out, rather than pumping out content for its own sake. Entity analysis is a central part of how topic mapping should be done today, so do not skip it. It also powers entity-driven feedback and review analysis (mapping how positively customers talk about specific aspects of your service, your shipping, billing, pricing, support) and digital-PR tracking (measuring the sentiment your brand is mentioned with across ranked coverage).

A couple of these deserve a closer look, because they are where entity analysis earns its keep fastest. On SERP entity analysis, once you have scraped the ranking titles, meta descriptions and URLs for a set of branded or topic queries and run extraction over them, you can build a table of which entities appear across the top results, how often, and with what sentiment, then hunt for patterns: do pages that mention a given entity in a positive context tend to rank higher, which entity types dominate the niche, and which entities your own pages are missing entirely? That last question is an information-gain goldmine, the entities your competitors never mention are your opportunity to become the unique source on them. On entity-driven review and feedback analysis, you extract the entities customers mention (shipping, billing, a specific product, a member of staff) and the sentiment attached to each, giving you a precise map of which aspects of your business people love and loathe, insight that is as useful to your product and content teams as it is to SEO. In both cases the pattern is the same: entities turn an unreadable pile of text into a structured picture you can actually act on.

Ten practical ways to implement entity analysis in SEO and digital marketing

The overview above shows the breadth. Here is how each project actually works, step by step, so you can pick one and run it this week.

1. Use entity analysis in keyword research

Entity analysis beats traditional keyword strategies by focusing on the context and relationships between words, thereby helping you gain a deeper understanding of the context and importance of words.

Entity analysis revolves around the concept of identifying and leveraging ‘entities’, which are distinct, well-defined terms or concepts, within your content strategy. This approach is crucial because search engines like Google are increasingly focusing on understanding searcher intent and the contextual meaning of queries. By incorporating entity analysis, you can align your keyword strategy more closely with how modern search algorithms interpret and categorise content.

Instead of simply building a keyword universe, you should also build an entity universe for your website’s topic, where you delve into your niche topic and identify not just popular keywords, but also the entities that are frequently associated with them. By mapping out these entities, the relationships between them, and how people search (their search patterns, like n-grams), you can create content that is not only rich in relevant keywords but also structured in a way that search engines recognise as authoritative and comprehensive. Having this mapped out can also enable you to plan unique content, and the new information associated with your target entities that your content will introduce.

To implement entity analysis in your keyword research, build a keyword universe the traditional way, then use Google’s Natural Language API to help you identify entities within your topic area and understand their relevance and relationship to each other. This can help you see the relationship between particular entities and search volume, as well as search competitiveness. This method is particularly effective for long-tail keyword strategies.

Using your keyword data as a starting point, validate which keywords contain entities. Then:

• Create content maps based on closely linked entities.
• Create lists of positive and negative secondary entities for content writers to discuss.
• Create lists of entity attributes to address in the content (the EAV model from earlier is your friend here).
• As an additional step, you can also validate which keywords contain entities that are part of Google’s Knowledge Graph.

2. Use entity analysis in internal link or backlink audits, and to optimise site structure

Entity analysis can be great for improving your site architecture, especially in the context of internal link analysis. You can incorporate entity analysis at several different points of your internal link audit or when analysing your backlinks:

• Extract entities from your internal link anchor text to check whether it is aligned with the topical entity map of your site and the entities your target users search for or talk about online. For example, if your website is focused on sustainable living, entity analysis can help you determine if your internal links are consistently emphasising related entities like ‘eco-friendly products’, ‘sustainable practices’, or ‘green living tips’.
• Extract entities from your backlinks’ anchor text to check whether your content is being described by third parties in the way that you intend it to be. Are there any repeating entities that describe your content in ways that you would not consider? Are these aligned with the way that your content is intended to be read and understood? If not, perhaps this can indicate the need to rework some aspects of the work.

This approach of analysing both internal and external links through the lens of entity analysis not only enhances the coherence of your site’s thematic structure but also ensures that the narrative you’re building online is consistent and aligned with your brand’s goals. In a digital environment where relevance and context are king, fine-tuning your site architecture using entity analysis can significantly impact how your content is perceived by users, third-party websites, and search engines.

3. Use entity analysis to analyse feedback from your customers or users

In my post on practical ways to implement sentiment analysis in your digital marketing or SEO strategy, I shared how you can use sentiment analysis to analyse customer feedback or user interactions on social media websites and comments. Entity analysis with Google’s Natural Language API can take this analysis a step further, as it can provide a more granular understanding of your customers’ feedback.

Imagine that you not only analyse customer feedback for sentiment polarity, like whether a review is positive or negative, but you also go into the nuances of the particular aspects that make up the review. If a customer’s review is negative, you’d like to know which aspects of the service or product bring the most negative sentiment, and conversely, the aspects of what already works well. For instance, they like the customer service but are unhappy with the pricing structure. This information can be pivotal in addressing their concerns or amplifying to other customers what you are doing well.

Google’s Natural Language API identifies not only entities in the text but also the sentiment and sentiment magnitude associated with them. The sentiment magnitude feature of the API adds another layer of insight, showing not just whether the sentiment is positive or negative, but also the strength of that sentiment. This helps in prioritising which issues need immediate attention based on how strongly customers feel about them.

Use the template and process shown earlier in this post, then check if there are entities that are mentioned more times than others, and look for patterns in the sentiment with which they are mentioned. You can use this analysis for direct feedback (like customer forms or product reviews) but also for feedback about your company collected on other platforms (like Trustpilot, Glassdoor, or your Google Business Profile reviews).

By employing this advanced entity analysis, you can pinpoint the exact features or aspects of your service that are being talked about. This method moves beyond basic sentiment polarity, allowing for a detailed analysis of each aspect of a customer’s review. Such detailed insights are invaluable for product development, customer service improvement, and targeted marketing strategies, ultimately helping businesses better align with their customers’ needs and preferences. I walk through a full version of this, end to end, in the semantic analysis of customer reviews tutorial.

4. Use entity analysis for content analysis and new content ideation

Entity analysis is a powerful tool not just for keyword research, but also for dissecting and understanding the content structure of large websites. It offers a unique lens through which you can analyse how content is organised, the themes it covers, and the depth of coverage on specific topics. For instance, by conducting entity analysis on a comprehensive blog site, you might discover that certain entities are heavily focused on, while others are only briefly touched upon. This can reveal content gaps or areas that are potentially over-saturated, signalling new content to be created, or content to be consolidated.

Entity analysis can spark ideas for new content. By identifying underrepresented entities within your niche or on your website that are otherwise important to customers or the business messaging, you can uncover opportunities for creating new content that addresses these areas. This approach ensures that your content remains fresh, relevant, and comprehensive. It can also help in differentiating your content from competitors by focusing on unique or emerging entities that have not yet been extensively covered.

Entity analysis can also be used to refine existing content. For example, if entity analysis shows that certain topics are commonly discussed in conjunction with others, but your content only addresses them in isolation, there may be an opportunity to expand or update these pieces to provide a more holistic view.

In practice, tools like Google’s Natural Language API or similar entity analysis tools can automate the process of understanding, or even classifying, the content on your website. Starting with your website content (scraped), extract entities from the text, titles, and meta descriptions. If an entity appears more than a certain number of times on a page over a certain threshold of words, highlight it as the prominent entity for the article. Then ask yourself these questions:

• How many articles with prominent entities have them in the title or meta description?
• How many articles with prominent entities have internal links incoming from other articles where the same entities are mentioned? This can signal internal link opportunities.
• Highlight articles that don’t contain any entities and map them against traffic. This can signal content enhancement or content consolidation opportunities.

Entity analysis informs your content strategy by revealing key entities and their context, helping identify areas for improvement or new topics, and ensuring alignment with audience interests.

5. Use entity analysis to analyse YouTube comments and other social video interactions

Using entity analysis on platforms like YouTube can transform the way you interpret viewer comments, turning them into a goldmine of customer insight. By analysing comments on videos or on other social platforms, you can identify key themes and topics that resonate with your audience.

For instance, by analysing YouTube comments, entity analysis can reveal the most talked-about features of a product review video or the aspects of a tutorial that are most appreciated. These insights are crucial for content creators to tailor their future videos to audience interests, enhancing engagement and viewer satisfaction. The same applies to TikTok and Instagram comments, and to video and podcast transcripts, which are long-form text in disguise.

6. Incorporate entity analysis into competitor analysis and identify content gaps

Consider a scenario where you’re analysing a competitor’s website. How about five competitors? Speed is of the essence to get ahead of competitors if you’re starting a website’s content from scratch or trying to reverse a traffic drop, and entity analysis allows you to quickly understand what competitor content is about and what the main themes of their website are, allowing you to see whether they heavily focus on certain topics but completely overlook others that are equally relevant to the audience. This gap presents a golden opportunity for your brand to create content that fills this void, thereby capturing a segment of the audience that’s currently underserved.

Before implementing entity analysis, you need to scrape the content of your competitors. Then, Google’s Natural Language API can automatically identify and categorise entities within large volumes of content, giving you a clear picture of the competitive content landscape.

By understanding the entities that your competitors focus on, you can assess whether these align with the interests and needs of your target audience. If there’s a discrepancy, it could indicate that your competitors are missing the mark, providing you with a chance to create more audience-aligned content. This not only gives you an edge in content creation but also ensures that your content strategy is both unique and highly relevant to your audience.

7. Enhance local SEO with geo-specific entities

Incorporating geo-specific entities into your content can significantly improve your local search performance. I’ve written about the importance of doing intent-driven keyword research for international SEO. By tailoring your content to include local landmarks, events, or terms that only people in local markets use to describe them, or even addressing region-specific issues, you can improve how relevant your travel website is for local markets, for instance. This approach is particularly effective in attracting a regional audience, as it aligns your content with the specific cultural and geographic nuances of your target area.

8. Use entity analysis for on-page and structured data enhancements

Entity analysis is also great for refining on-page content and enhancing structured data. By understanding and incorporating relevant entities into your website’s content titles, headings, and other page metadata, you can significantly improve how search engines comprehend and rank your pages.

On-page content optimisation using entity analysis involves identifying key entities that are central to your content and ensuring they are prominently featured and accurately represented. This goes beyond traditional keyword stuffing; it’s about creating rich, contextually relevant content. For example, if you run or plan the content of a culinary website, entity analysis can be instrumental in identifying a broad range of related entities that extend beyond just recipes and ingredients. It can highlight aspects like cooking techniques, kitchen tools, cultural contexts of various cuisines, and dietary considerations.

Structured data, or schema markup, is another area where entity analysis can have a significant impact. By identifying and tagging entities within your content, you can enhance your structured data, making it easier for search engines to understand the context of your pages. This is particularly beneficial for rich results, featured snippets and AI-generated answers, where precise entity recognition can influence how your content is displayed and whether it gets cited. If you want to go further, read how structured data reinforces entity understanding in AI search.

9. Improve the topical authority of your website’s content by building a topical entity map

Entity analysis can be used for topical mapping. This process involves identifying and structuring key entities related to your niche, ensuring that your content comprehensively covers these topics in depth and breadth. By doing so, you establish your site as a go-to resource, demonstrating expertise and authority in your specific field.

A topical entity map helps you visualise the landscape of your domain, showing how different topics and subtopics interconnect and relate to each other. Content that aligns with a well-structured topical entity map is more likely to satisfy user queries comprehensively, leading to better user engagement, longer time on page, and greater satisfaction of your website visitors. These signals are amazing not only for your bottom line but also for signalling to search engines that your website offers a high-quality experience and helpful content.

Building a topical entity map starts with in-depth research into your niche to identify key topics and subtopics relevant to your audience. This involves analysing your own content, competitor sites, industry forums, and social media to understand the range of subjects covered in your field. Entity analysis tools like Google’s Natural Language API can aid in this process by extracting and categorising entities from large volumes of text, helping you identify the most prominent and relevant topics.

Once you have a list of key topics, organise them hierarchically to reflect their relationships and relevance. This can also be visualised in a tool like Data Studio, or via a mind map or knowledge graph. The primary topics should represent the core areas of your niche, with secondary and tertiary topics branching out to cover more specific subtopics, while still remaining relevant to your core entities. Regularly updating this map as trends and audience interests evolve is crucial for maintaining the relevance and authority of your content.

10. Use entity analysis in your SERP analysis

Finally, point the same process at the search results themselves. Collect the titles and meta descriptions that rank for your target keywords, run entity extraction over them, and you’ll see which entities Google keeps surfacing for a topic, how often, whether they appear in titles or only in descriptions, and with what sentiment. It is one of the fastest ways to find a content direction, and to uncover entities your keyword tool never showed you. I cover the full workflow, alongside competitor categorisation, sentiment and n-gram analysis, in 4 ways to boost your SERP competitor analysis with machine learning, with a runnable notebook in the MLforSEO code hub.

The Role of Entity Analysis in AI Search Systems

Entities matter more every year, because AI search does not really rank documents so much as it assembles answers from facts, and facts are entities and their relationships. I go deep on this in my iPullRank article, Entity Recognition in AI Search, and a few points are worth carrying into your own work. Retrieval happens at the passage level, so each section of your content needs to be semantically complete in isolation, able to stand on its own when an LLM lifts it out of context. Entity linking then resolves ambiguity: canonical identifiers like Wikidata Q-numbers let systems deduplicate synonyms and know which “Jordan” you mean, and schema markup with persistent @id and sameAs links makes your facts groundable and reusable by search systems and agents.

The strategic shift follows from query fan-out: because these systems generate dozens of synthetic sub-queries, content organised into hubs around priority entities, with supporting spokes, captures far more of the reasoning chain than keyword-focused pages ever could. And measurement moves with it, from tracking URLs to tracking entities, their performance, their schema validity and their ambiguity rates. It all reinforces the point from the previous section: for extraction at scale, reach for a task-specific NER API, not a chatbot, because production pipelines need the richer, consistent metadata only purpose-built models return.

Key takeaways

Entity analysis is a powerful machine learning approach that can be effectively employed to enhance various aspects of a digital marketing or SEO strategy. Despite there being many ways to get started, one of the easiest for beginners is via the Google Natural Language API in Google Sheets, which requires no coding skills. Once you have the analysis completed, you can incorporate the insights from it into various projects.

Entity analysis in digital marketing enriches keyword research by revealing context and relationships between terms, aiding in content creation. It helps identify gaps in competitive analysis for differentiation opportunities. In on-page SEO and structured data, it ensures content is contextually rich and search engine optimised. For social media, like YouTube comments, it offers insights into audience preferences, influencing content strategy. It enhances local SEO with geo-specific entities and improves website topical authority by creating detailed entity maps, making digital marketing strategies more targeted and effective. And as search shifts towards AI-generated answers, the entity lens stops being a nice-to-have: it is how these systems read your content in the first place.

Where to Take Entity Analysis Next

This post covers what entity extraction is and where it fits. The Entity Extraction and Analysis module inside Introduction to Machine Learning for SEO then goes hands-on: comparing NLP APIs against generative AI, query entity extraction, semantic analysis of customer reviews, and entity-based internal linking. Read the companion post on which NLP API to actually use next, and see it applied in the customer-review analysis walkthrough.

Related glossary terms

• Entity
• Entity extraction (NER)
• EAV model
• Entity salience score
• Entity sentiment analysis
• Sentiment magnitude
• Google Cloud Natural Language API
• Knowledge graph