NLP is a kind of artificial intelligence technology that tries to assess and analyse human language. NLP aids our computers to understand, interpret, and replicate human language characteristics.
In an age governed by technology, it’s important for computers to be able to understand us. NLP is an attempt to take our shoddy language inputs and turn them into something useful computers can interpret and respond to.
Since machine learning algorithms have been introduced, computers now process huge chunks of data to identify certain patterns to better understand human languages. This has numerous use cases for tech, many of which you see and use on a daily basis:
• Voice assistants (Alexa and Siri)
• Email filtering (identifying spam)
• Content classification (social listening)
• Search engines(Google, Amazon, Firefox)
Unless you have been living under a rock for the past couple of years, you probably heard about Google BERT in late October last year. BERT (Bidirectional Encoder Representations from Transformers) is an NLP model that Google introduced in 2018 and began rolling out in October 2019. BERT can consider the full context of a word based on the words that come before or after.
In October 2019, Google announced the official BERT Algorithm release.
According to Google's statement, a fully rolled out update affected 10% of all search queries.
Three months later, in the beginning of 2020, we’ve already had a first significant Google's core update this year called January 2020 Core Update.
Every year, there are hundreds or thousands of updates, but this particular update's pre-announcement before the actual rollout was quite distinctive.
According to Google:
“These improvements are oriented around improving language understanding, particularly for more natural language/conversational queries, as BERT is able to help Search better understand the nuance and context of words in Searches and better match those queries with helpful results.”
USING NLP TO OPTIMIZE CONTENT
First step: Find a suitable keyword.
A suitable keyword will enable you to search for the subject or niche you want to target. That will also make sure that the results which show up are relevant to what you need to target that specific niche.
But finding the correct keyword in context to your need is essential as well. One word can show thousands of results which in the end may or may not be linked or related to your subject or niche of desire. Also make sure that you pick a word which has high search volume when using SEO tools.
Second Step: Use basic search to find top searched sites.
Here just simply take your keyword and search for it on Google search engine. It will obviously show you multiple results but your job is to only look at the first few on the page that comes up. The top-ranked pages are the ones that contain content of the highest quality, which is of benefit to you in this case.
Third Step: Using Google NLP
Copy some of the content from one of the opened sites and paste it in the dialogue box here and Analyse it. The content will then get split into 4 distinct categories, consisting of:
• Entities: to oversimplicate it, an entity is a thing. An entity can be anything. A place, person, organization, idea, or concept. Entities define the relationships between things and help search engines like Google understand their relativity.
• Sentiment: Sentiment Analysis is a sub-field of NLP that aims to identify opinions and emotions about an entity within a text.
• Syntax: Syntax gives us valuable insight into how Google sees our sentences and categorizes words for understanding. Syntax analysis provides far fewer implications for content and SEO as our other three models. Good grammar and clear sentence structure is key to satisfying readers, but it’s unlikely you would glean much more than that from syntax analysis. In fact, if you use Grammarly or another AI writing assistant tool, it’s likely you’re already using syntax analysis to optimize your content.
• Categories: Categories reveal how Google classifies text. Within their API demo, you will see the category along with a confidence score. It goes without saying that you would want your content to produce a high confidence score for the category and subcategories you wish to rank in. As with entities, it’s unlikely there’s a tried and true process to optimizing for classification. However, categories can also serve as a great guide. I’d recommend analysing entity analysis of top-ranking pages prior to crafting content and using category classification as more of a post-writing check.
Fourth Step: Google spreadsheet.
Use the data you’ve procured and enter it according to the importance it has regarding your niche.
Fifth Step: Use this data to optimize content.
Using this data wisely to get ahead of your competitors seems like a very obvious choice. Again, It may seem obvious, but many SEO experts think every website listed in the top ten is a page they compete with. This kind of approach may dilute your dataset, so I strongly recommend treating this step seriously.
To select them properly you need to:
1. Define your content type. Even if you’re going for a “research” search intent, you need to know if you’re writing a blog post, creating a video, or a landing page.
2. Exclude pages that serve different intent.
3. Exclude outliers: pages that are much longer or much shorter than other ones.
4. Exclude pages that rank because of their authority and backlink profile especially, in the case of one-sided SERPs, be aware of using the preferred sentiment since using the reverse can be a hand brake for your SEO efforts. An easy example would be if all top search results of a product with positive reviews, and you created a negative review, that might negatively impact your rankings. Google has taken the historical data of the more relevant sites and apparently it favours positive reviews over the negative ones. Even though Google experiments with the sentiment and, in many cases, it fluctuates over time, it can be highly challenging to get into the top ten with an unexpected undertone. Similarly, sometimes it is impossible to overtake one of the highest organic positions in Google with the content short on entities' appearance. Comparing yourself with direct competitors allows us to fulfill the content gap, estimate the user's intent behind the search query, and make the content more comprehensive and complementary.
Our Take On NLP and The Future of SEO
NLP is a very complex concept. You don't need to be a machine learning expert to utilize available tools to enhance the SEO methodology. Let the tools do the hard work for you. But at the same time, having some basic knowledge, which is confirmed by SEO tests and observing fluctuations will help you to keep up with the industry. Though NLP doesn’t offer specific proven formulas for better ranking, it is likely the future of SEO. It’s important that SEOs have a baseline understanding of NLP models and how Google processes language. Keep your eyes on future models Google adds to their algorithm, and don’t be afraid to tiptoe into the waters of machine learning and AI. It’s where our industry is headed.