How To Find NLP Keywords For SEO

• updated on
July 23, 2023

Whether you're just starting your SEO journey or are a seasoned professional, you can count on one certainty: The landscape of SEO is ever-changing. If you're looking for a field where you can learn all the "right" strategies and then use them predictably and repeatedly forever, you're in the wrong place.

If, however, your brain lights up at the prospect of continually solving puzzles and adapting to advancements in the fascinating trajectory of search engine evolution, then read on and let's talk about NLP, one of the more complex facets of search engine optimization.

Since effective SEO always involves a combination of multiple approaches, some strategies are more straightforward than others. Maintaining a clean backlink profile, for instance, is something reasonably cut and dried. Using NLP techniques to your advantage, on the other hand, requires a fair bit of background knowledge, research, and intuition in understanding human language. Fortunately, there are also some excellent tools available to help you along the way.

In this post, I'll try to give you a solid understanding of some of the basics of NLP SEO so that you can begin integrating NLP strategies into your own SEO framework.

What Does NLP Stand For & What Is It?

NLP stands for Natural Language Processing, the research field focusing on transforming natural language into machine-computable information.

Linguistic AI capabilities have been rapidly advancing since the advent of search engine technology, and what would have seemed groundbreaking even just a few years ago is already obsolete. Gone are the days when Google worked with statistical models built around keywords. Today, NLP technology is focused on refining machine learning models to understand and dissect human language in increasingly sophisticated and comprehensive ways.

NLP takes the less tangible elements of text—such as tone, purpose, intent, and context—under close scrutiny and attempts to deconstruct them using powerful algorithms. The end goal, of course, is to find the magic formula that mimics the human mind as closely as possible—with all the power of artificial intelligence behind it.

Indeed, it sounds a bit like sci-fi. But the truth is that the more Google's algorithms understand human language, the more successful Google will be.

The BERT Update

At the end of 2019, Google announced the official BERT algorithm release. BERT (which stands for Bidirectional Encoder Representations from Transformers) was a game-changer in the SEO world because it was focused on quality content, context, and natural language processing. Since then, search engine optimization strategies have had to evolve to reflect BERT's criteria.

The Primary Goal of Search Engines

Before getting into how BERT changed SEO, it's important to understand the primary goal of search engines like Google.

To quote Milosz Krasinsksi from Oncrawl, "The best way to understand NLP from an SEO perspective is to first understand BERT. [...] BERT contains two major components: Data (pre-trained models) and methodology (defined ways to learn and use those models). [...] BERT collects specific sets of data relating to content and then learns how to analyze that data. NLP is BERT's brain. It's able to understand the word or phrase in its context by looking at various signals around it."

The signals Krasinsksi is referring to include, but are not limited to: The words directly preceding and following a particular keyword; subsections of pages as well as entire pages; and sentences, paragraphs, and queries as a whole.

As you can imagine, this NLP is different and drastically more nuanced than older algorithm models, which were more simply focused on keywords alone. Google is no longer so invested in isolated keywords. Rather, it’s looking to pinpoint tone, intent, and sentiment in context.

How Does Google Use NLP Keywords?

To be fair, whole generations of people have now grown up relying on Google (and other search engines) to meet their daily needs. The Google users of today are less patient and more exacting than their counterparts from a decade ago, and Google has to keep evolving to meet the demands of more complex queries and higher search engine expectations from the public.

In the end, it all boils down to the search quality that Google can yield.

As a result, Google is working extremely hard to understand the intent and context of search queries and web content. In other words, to understand natural language.

Using NLP, BERT measures sentiment. That's right. The Google algorithm scores texts to determine whether they’re positive, negative, or neutral. Positive sentiment is scored between 0.25 and 1, while negative sentiment is given a value between -0.25 and -1. Neutral sentiment falls in the numerical range between -0.25 and 0.25.

From an SEO perspective, this is significant because if BERT has scored all your highest-ranking competitors' pages as positive (i.e., their pages contain positive words and phrases), then your page had best be positive, too in order to compete. Obviously, this is a little over-simplified, but it essentially illustrates the SEO power that can be harnessed by understanding sentiment, as defined by BERT.

Are NLP Keywords a Ranking Factor?

Yes. NLP keywords are a ranking factor. They’re only one piece of the SEO puzzle, but they can definitely have the traction to move your rankings in the right direction.

NLP essentially interprets BERT's metrics to help Google understand what makes better content. If you can optimize your content accordingly, you'll reap the rewards in higher rankings. All other factors being equal, if you can directly compare your page to the top-ranking pages and optimize your content to reflect the same NLP keywords, then you should be able to rank highly too. Of course, you'll also need to take factors such as context and sentiment analysis into consideration.

NLP Vs. LSI Keywords

Alright, before we move on to specific strategies, let's clarify one more concept: LSI keywords.

LSI (latent semantic indexing) is something of a contentious term in the SEO industry. Often used loosely to mean almost any technology used for the purpose of semantic analysis, LSI is actually one specific implementation of language processing. Technically, it falls under the broader umbrella of NLP.

Developed in the 1980s, LSI focuses on AI learning a wide variety of synonyms based on context. It uses mathematical techniques to find relationships between words.

The contention largely stems from people using the term LSI incorrectly. While some SEO trend-setters do still refer to LSI as an important ranking factor, most feel that it’s outdated and effectively replaced by Google's more modern NLP.

Either way, the fact remains that semantic analysis remains an important factor in the search optimization world.

Understanding Entities in SEO

Let's take a moment to understand the term entity. As defined by Google, an entity is anything that’s "singular, unique, well-defined, and distinguishable". Like a noun, an entity is often physically tangible but doesn't have to be. It could be something abstract, like an idea, relationship, or historical event.

Entity-based SEO builds on search engines' attempts to generate the most accurate results by connecting user intent, context, and the relationship between words. In practice, this means that search results are often generated based on words or phrases that are connected to a particular entity—but that doesn't specifically name it (sometimes known as identifiers).

For example, if you type "famous tv show about doctors" into Google search, you'll come up with a list of TV shows that include doctors in them. In this case, these show titles are the entities, but the search engine has used NLP technology to identify a word or phrase directly related to the subject.

Google's NLP picks up on words related to entities in fairly subtle ways, including salience—their relevance or importance in a particular text—and ranks them in order of importance to the context of the page. Like sentiment analysis, saliency analysis is recorded numerically. It ranges from 0.0 for no saliency up to 1.0 for highly salient identifiers. Entities are also ranked by category, which is a more familiar concept for those in the SEO trenches.

Entities mark a shift away from keywords as the primary focus behind Google searches. Using NLP, Google is aiming to build contextual connections and minimize ambiguity to improve the quality of its search results.

As an example, if someone types a single keyword search such as "banana" into a search engine, it’s impossible to know whether their intent is to find out about the vitamins in bananas, banana recipes, or banana plants. A search such as, "fruits high in potassium", however, might turn up a wealth of relevant information about the health benefits of bananas—if Google's NLP can pick up on the intent behind the query.

Mobile searches and voice searches through digital assistants are also much easier with entity SEO. At SerpFocus, we believe it’s important to recognize this trend and use it to leverage your own SEO.

How Do I Identify NLP Keywords To Use in My Content?

As I mentioned at the beginning of this post, there are some great tools that can really help you to optimize your content using NLP keywords. Thank goodness!

One is Google's Natural Language API. This API is a comprehensive tool that carries out entity analysis, sentiment analysis, content classification, multi-lingual analysis, plus more. It allows you to plug into and examine any text, providing you with valuable NLP data. Not only can you transparently see how Google has analyzed your content, but you can also see how it has analyzed the content of high-ranking pages in your niche. As they say, knowledge is power. Once you've identified the gaps, start filling them in.

Using Surfer SEO

Surfer is probably the gold standard when it comes to identifying specific NLP keywords to optimize your content. By linking to Google's NLP API, Surfer provides users with the words, phrases, and sentiments that Google has identified as significant in relation to any given entity. It also analyzes top-ranking pages so you can see how often you should be using various NLP entities and identifiers in your content. This is an incredible feature and takes so much of the guesswork out of NLP SEO! Read my Surfer SEO review here.

surfer input

Are There Any Risks With Implementing NLP into Your SEO Strategy?

As with any new endeavor, the biggest risk of implementing NLP into your SEO strategy is not doing proper research. But done right, I can only see NLP SEO benefitting you and your business.

While more traditional ranking factors remain highly valuable, the truth is that Google is embracing user intent and search relevancy in a major way...which means that those of us in the SEO industry probably should be as well.

Jumping into NLP SEO may feel intimidating, but if you can be one of the earlier birds to get in on this trend, it could have substantial rewards! My advice is definitely to add it to your toolkit asap. I don't believe NLP is on its way out. To the contrary, I believe it will only continue to evolve and gain momentum.

Other Use-Cases

To finish off this post, let's take a look at some specific examples of how you can leverage NLP to your advantage as a business owner. As we know, there's no silver bullet in SEO. But a combination of these strategies could be a great starting point for refining your approach to NLP SEO.

Structured Data Markup Automation

Structured data markup is a machine-readable representation of your product data directly on your site. By extracting entities and their unique identifiers, you can optimize your structured data markup for NLP. This is called structured data markup automation and will help search engines index your website more effectively.

Mega-sites like BBC, Reuters, and Eventbrite are employing this strategy to increase visitors to their web pages with great success. Why not use it yourself?

Internal Linking

We all know about the importance of backlinks (external links) in SEO. But, with the advent of NLP SEO, internal linking has also become a major—and impactful—focus. While authority and relevance remain important factors for effective link-building, context, link structure, and placement have also joined them in the front seat.

Internal links serve a few purposes. They help users discover content from your website, they improve user experience and the length of time new visitors spend on your site, and they help search engines evaluate your content and whether the user experience it provides is effective or not. A strong, logical internal linking structure significantly helps your SEO. There are tools that assist you with the internal linking process, for example read my review on Link Whisper.

To maximize your in-link building, you can extract the entities from your content and then create (and internally link to) more content based around them. This can be an opportunity for you to increase the organic content on your page while also expanding on concepts in-house that visitors might previously have had to leave your page to find. For example, you might decide to add an introduction to a topic, some definitions, or further information about a relevant entity.

This sort of content bolstering can be a win-win-win. If you pull it off well, it allows you to expand and improve your website (with more links and more information), keeps visitors engaged longer, and is also a savvy NLP strategy for boosting your Google rankings.

Content Recommendation

Content recommendation is the practice of using machine learning algorithms to predict what a user might like to read next. Done right, it can dramatically increase the number of time visitors spend engaged on your website—and, thus, your rankings.

NLP can inform machine learning models that help users jump from one engaging article to the next. This intersection between NLP and machine learning (among other elements) is known as Semantic AI. If you’re interested in seriously integrating Semantic AI into your SEO strategy, SerpFocus recommends checking out the PoolParty platform. PoolParty is an award-winning Semantic AI platform designed to help you build smart applications and systems.


If you've made it here to the end with me, I hope this post has been helpful. NLP is a complex topic, but one very worth delving deeper into if you really want to get to the core of how search engines are evolving—and how SEO is evolving as a result.

Understanding NLP gives you more insight into how your page looks through Google's eyes. Like anything, its power can be harnessed to your advantage. But you have to take the time to do proper research and optimize your content accordingly in smart and relevant ways.

In this post, I've covered some basic concepts about NLP SEO, but they're only intended as jumping-off points for you to explore further, research, and experiment with. We'd love to hear more about your experiences with NLP-based SEO strategies.

Let us know about your successes, challenges, and—especially—your questions. At SerpFocus, our aim is to provide SEO and marketing support, so don't hesitate to reach out, no matter where you're at in your SEO journey.

From The Author

Terry Williams

With over 10 years optimizing sites, I've boosted search visibility for brands through customized strategies. Currently, I develop effective SEO solutions for a top agency, immersed in the latest trends and innovations. Read my full bio.

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