Is NLP in SEO All That It’s Advertised to Be? [+ How to Implement It]

AI for SEO (especially, NLP in SEO) has been a hot topic for the last couple of months. Just a quick look at Google Trends will show us that there’s been a meteoric rise in the public’s interest in AI in general and in ChatGPT in particular.

So, in this post, we’ll put our MythBusters hats on and look for a real answer to a common question. Is NLP in SEO all that it’s advertised to be?

We’ll cover:

• What’s NLP
• NLP’s limitations
• Common uses for NLP in SEO
• How NLP is used in Google search and other search engines

Ready? Let’s dive in!

What’s NLP?

Let’s briefly cover:

• A definition of NLP
• The fundamentals of how NLP works

NLP Definition

NLP, or Natural Language Processing, is a branch of artificial intelligence that focuses on the interaction between computers and human language. 

In the context of SEO, NLP can be analyzed both as a tool for content creation and for content curation and ranking.

Leading search engines use NLP to: 

• Understand user intent and deliver relevant search results
• Interpret and analyze complex search queries
• Power voice assistants by processing spoken queries and generating voice-based search results

Meanwhile, SEO teams can use NLP to:

• Generate meta tags, snippets, and content summaries for improved search visibility and user engagement
• Analyze users’ content consumption preferences
• Understand and process content in multiple languages, facilitating international search engine optimization efforts
• Assists in optimizing content by analyzing keywords, context, and user behavior to enhance search engine rankings
• Deliver better content results for internal searches

In essence, NLP empowers marketers to craft custom user experiences, drive better SEO outcomes, and connect with their audience effectively.

How NLP Works

Overall, NLP combines linguistic rules, statistical models, and machine learning algorithms to process and analyze text data. Thus, enabling computers to “understand”, generate, and interact with human language in a meaningful way.

Here’s a more in-depth explanation of how NLP works:

Tokenization

Tokenization is the process of breaking down text or data into smaller units. These units are called tokens. Tokens can be individual words, characters, or even smaller units. 

Tokenization converts unstructured data, such as text, into a structured format that can be used for further analysis and processing. Tokenization is typically performed by splitting the input data based on specific rules or patterns, such as whitespace or punctuation marks.

For example, consider the sentence: "Machine learning is fascinating!" Tokenization would break it down into the following tokens: ["Machine", "learning", "is", "fascinating", "!"]. Each token represents a separate unit of meaning that can be processed by machine learning algorithms.

Vectorization

Vectorization is the process of representing data in the form of numerical vectors, which can be understood and processed by machine learning models. In the context of natural language processing (NLP), vectorization is used to convert textual data into numerical representations that capture semantic and contextual information.

One common approach to vectorization is the bag-of-words model. In this model, each tokenized word from the input text is assigned a unique index, and a vector is created with a length equal to the vocabulary size. The vector is initialized with zeros, and each element of the vector represents the count or presence of the corresponding word in the input text.

For example, using the tokenized sentence from the previous step, the vectorized representation might look like this: [1, 1, 1, 1, 1]. Here, each index of the vector corresponds to a word in the vocabulary, and the value at each index indicates the presence or absence of that word in the sentence.

Prediction via Neural Networks

Neural networks are a class of machine learning models inspired by the structure and function of the human brain. They are particularly effective in learning patterns and relationships in complex data, such as images, speech, and text. Prediction via neural networks involves training a neural network model on a labeled dataset and using it to make predictions on new, unseen data.

In the context of NLP, a common neural network architecture is the recurrent neural network (RNN) or its variants, such as the long short-term memory (LSTM) or the gated recurrent unit (GRU). These architectures are well-suited for processing sequential data, such as sentences or paragraphs.

During the training phase, the neural network learns to map the vectorized input data to the corresponding output labels. This is done by adjusting the weights and biases of the network through a process called backpropagation, where the model computes the gradients and updates the parameters based on the prediction errors.

Once the neural network is trained, it can be used to make predictions on new, unseen data by feeding the vectorized inputs into the network and obtaining the predicted outputs. The prediction is usually a probability distribution over different classes or labels, and the final decision can be made by selecting the class with the highest probability.

Overall, the machine learning process involves:

• Tokenizing the input data
• Vectorizing it to numerical representations
• Training a neural network model to make predictions based on the vectorized inputs

These steps form a fundamental pipeline for many NLP tasks, such as sentiment analysis, text classification, and language generation.

NLP’s Limitations

The most common application for NLP in SEO is AI copywriting. In short, SEO teams use tools such as Jasper, CopyAI, or any other GPT-powered writing assistant to produce content at scale.

However, that’s usually a terrible idea.

Why you can’t produce quality content with NLP

NLP models are content aggregators that answer queries based on the material they’ve been trained with. However, these models:

• Don’t have a deep and sensible understanding of language
• Can’t exercise judgment to distinguish between fake/irrelevant and real/relevant information
• Are unable to synthesize different ideas to produce original concepts
• Lack an aesthetic perspective on language, which can render their writing style flat and clumsy
• Can’t struggle to weave readers’ needs into their output - especially when it comes to subjects with a limited database

In short, NLP models are not human or conscious. So they can’t craft complex messages that resonate with a specific target audience. 

Common Use Cases for NLP in SEO

Modern SEO teams count on NLP to:

• Create content
• Build backlinks at scale
• Perform keyword research

Although common, these use cases may not be ideal. So, in this section, we’ll dive into how NLP is commonly used and propose better, alternative applications. 

Using NLP to create content

As we covered in the previous section, machine learning models aren’t competent enough to create original content, unassisted and unedited. This is partially due to its lack of true critical and creative skills, but also due to its tendency to “hallucinate” events and data.

Instead of using NLP to produce material, we recommend using it to curate content experiences based on your existing, human-made material. For instance, you can feed your blog posts into a GPT-driven web app and offer it as a tool that users can tap into to learn more about your product or value proposition. 

Smart teams also use NLP to repurpose their material. AI-powered tools can be great for repackaging your written or audiovisual materials into other formats. 

To learn more about how you can use NLP for content repurposing, check out our content repurposing guide. 

Build backlinks with NLP

Teams usually tap into NLP to:

• Find potential link-building contacts
• Personalize outreach emails
• Write guest posts faster

Until recently, ChatGPT was unable to browse the internet. So when you asked it to produce a list of publications for link-building, it retrieved it from its existent training data, giving you a list of possibly outdated and reductive information. 

So, it wasn’t great for that. And, unless you gave it the necessary data to personalize your emails, an AI tool wouldn’t be able to figure out a way to add a human touch on its own. For instance, you’d have to explicitly tell ChatGPT that your prospect is a Star Wars fan, for it to personalize your outreach template accordingly. It’s not something the tool can figure out on its own. 

NLP-powered tools are great for speeding up processes, but not for creative thinking and problem-solving. You won’t get a final answer from them, but they can help you turn raw data into usable link-building assets.

Performing keyword research with NLP

Some teams use ChatGPT and other tools as keyword research assistants. While relatively competent, this use case can be a double-edged sword. 

AI tools will be able to generate a bunch of terms related to your query. But whether these terms have actual search volumes and truly match your users’ search intent is something that NLP tools won’t be able to assist you with.

You can add NLP to your keyword research process, but don’t make it your end-all-be-all tool. Instead:

1. Perform your basic keyword research on a regular SEO platform (such as Ahrefs or SEMRush)
2. Use GPT-3.5/GPT-4 to categorize your keywords by search intent
3. Use GPT-3.5/GPT-4 to come up with headlines and basic outlines for your content

How NLP Is Used in Google and Other Search Engines

In 2019, Google introduced BERT (Bidirectional Encoder Representations from Transformers). BERT uses NLP to help Google understand search queries in a more human-like manner. It considers search queries’ content and intentions by analyzing the words or sentences that usually come before and after them.

Additionally, Google uses sentiment scoring to evaluate the emotional undertone of a search query. If most highly ranked pages for a particular topic have positive sentiment while yours has negative sentiment, it may negatively impact your rankings.

BERT also considers entity, category, and salience metrics. Entities are tangible objects like people, places, or things. BERT identifies and evaluates these entities. Categories are important keywords that people frequently search for, and NLP categorizes text accordingly. 

What’s NLP’s impact on search?

Thanks to NLP, Google doesn’t have an inflexible and decontextualized understanding of search queries. This has allowed SEOs to move beyond keyword stuffing and shift their focus towards content quality, appeal and relevance - what we refer to as content-market fit. 

Quality Content by Our Best Humans

In this post, we explained how NLP works and explored its SEO uses and limitations. While extremely useful for streamlining content planning and distribution processes, NLP is yet to replace human creativity and insight. Especially when it comes to creating competitive content.

Looking for the right content partner for your brand? Discover why we’re loved by startups worldwide, book a free consulting call.