Natural Language Processing Techniques for Content Marketing

Natural language processing (NLP) and artificial intelligence (AI) have become a big part of how we gather, analyze, and convey information. They are an integral part of our smart world, from Google Home to auto-suggestions to email filters.

And they’re becoming an undeniable force in content marketing, too. It’s been six years since Google released its Hummingbird algorithm; content creation is focusing more on user intent and less about SEO.

Once used only for linguistic studies and translations, NLP now has a significant role in what kinds of content we create and how we create it. 

There are a lot of moving parts to NLP. Every topic or piece of text analyzed has to go through the same process of parsing, understanding, analyzing, and producing. 

Most of it is pretty technical and developer-oriented. So we thought we’d break down the basic steps of natural language processing into something a bit more approachable and apply it to content marketing strategy.

So here are the four fundamental steps of NLP and how they work at a high level.

Lexical and Syntactic Analysis

When an algorithm analyzes a piece of text, it starts by breaking up (parsing) a sentence, or text into grammatical units. At that point, the units are converted into tokens or bits of data a computer can read.

Then, it takes those tokens and tries to determine if the text is logical, given its grammatical structure. That’s a syntactical analysis.

If the structure is incorrect, it won’t be able to give you a logical meaning for the content. If it is, the algorithm will analyze the literal meaning of the text.

Of course, human language and structure are not always so cut-and-dry. You also need to consider sarcasm, a play on words, or imperfect grammar that mimics natural speech patterns.

That’s where semantic analysis comes in, which we’ll talk about in a minute.

Syntactic analysis is one component of natural language processing and helps algorithms and apps perform tasks like text analytics and language translation.

Semantic Analysis

Semantic analysis is a more sophisticated way of understanding the meaning behind a piece of text. This is where neural networks come in. 

Neural networks process data in a way that mimics the human brain. These big data processors learn language a lot like we do. It analyzes a piece of text and compares it to other human language patterns to find its true meaning. Then it groups fragments of text together, even if they don’t have much to do with each other on the surface.

Ever since Google released Hummingbird in 2013, it’s been pretty clear the search engine is using semantic analysis. 

In fact, Neil Patel teamed up with MarketMuse to study the algorithm by organizing millions of words of content into topic clusters and comparing them to 31.5 million page rankings. They found that pages that covered narrow topics very deeply did well in SERP rankings.

Because users are looking for valuable, in-depth information that answers their specific search queries, Google has worked hard to rank those kinds of pages first. It uses semantic analysis to understand relationships between topics, for example, spring garden care and mulching. 

It looks for content that covers related topics fully, either in one piece or through topic clustering, and ranks that content first.

Topic Modeling

Topic modeling refers to the process of analyzing unstructured data and clustering related words and phrases together. Basically, it takes semantic analysis a step further.

Once an algorithm has understood the meaning and relationships behind words and phrases, it groups them into related clusters. That process is called Latent Dirichlet Allocation (LDA).

Content marketers can use those clusters to map out their content plan and build more robust topic clusters for better ranking.

Let’s look at an example. Let’s say you’re a chamber of commerce in Vermont, looking to attract users to your site — and tourists to your state — during the fall months. You use a topic modeling algorithm to map out content related to fall foliage. 

So fall foliage in Vermont becomes your pillar piece. The algorithm will then give you related topics, like scenic fall drives, foliage maps, why leaves change color or fall festivals in Vermont. 

Basically, it’s taking the guesswork out of content planning.

Experts are pretty sure search engines like Google and Bing use topic modeling algorithms to search and rank pages.

I say pretty sure because search engines don’t like to tell us how, exactly, they rank pages. If they did, it would be pretty easy to game the system.

Marketers can also use topic modeling can start with a keyword and pull out related topics, subtopics, and alternative keywords for an individual piece.

Looking for real-world applications? MarketMuse uses semantic analysis and topic modeling to create briefs that cover everything I’ve talked about here. You can run an analysis and hand your writers a comprehensive assignment.

Named Entity Recognition (NER)

This is a great way to tag content for organization, content discovery, and SEO. Named Entity Recognition scans content to look for words that match predefined categories, like people, places, and things.

From those groupings, you can create tags that will help you search your own content, allow you to suggest related articles to users, and improve your metadata for search engines.

Imagine you have a vast library of content on your site. Suddenly, a topic starts trending on social media. It could be seasonal, like Halloween, or it could be something out of the blue like the death of a celebrity. 

If you’ve used NER to tag your content and fill in your metadata, it should be easy to find any related content you have on the topic, update it and republish it to ride the wave of that trend.

It also helps chatbots understand queries and pull up the right information for users. A chatbot will look at the words it is given, categorize them, and then pull up relevant answers from the same category.

Summary

Ever since Google switched its focus to user intent, content marketers have been trying to figure out just how to make the most robust and valuable content for their users and for search. NLP and AI help us do that by allowing us to study content from every angle. 

Using AI to inform and run your content plan isn’t really an option anymore. It’s simply the future. But think of all the exciting content you’ll put out there, content you may not have even known would help you attract and retain users