How to do Semantic Keyword Research Using NLP and Text Analysis
We’ve discussed what semantic search is in our previous post, What is Semantic Search?.
In this blog, we’re going to provide some insight into how you can start to ensure you’ve got semantic search at the forefront of your SEO strategy to stay in line with Google’s recent push towards contextual, intent based search results.
We’ve spoken about updates like Panda and Hummingbird and highlighted how important semantics in search is, in modern SEO strategies. We expanded on how Search Engines are looking past exact keyword matching on pages to providing more value to end users through more conceptual and contextual results in their service.
While the focus has moved away from exact keyword matching, keywords are still a pivotal part of SEO and content strategies, but the concept of a keyword has changed somewhat. Search strings are more conversational now, they are of the long-tail variety and are often, context rich.
So, how can we optimize for Semantic Search?
- Understand searcher intent
- Create quality content that answers questions and delights readers
- Build authority around topics
Traditionally, keyword research involved building a list or database of relevant keywords that we hoped to rank for. Often graded by difficulty score, click through rate and search volume, keyword research was about finding candidates in this list to go create content around and gather some organic traffic through exact matching.
While this method of keyword research is still relevant the landscape has changed. So a search phrase like “Machine Learning” can have multiple meanings and varied context.
- Machine Learning guide
- What is Machine Learning?
- Machine Learning in the enterprise
- Machine Learning Algorithms
And that’s just focusing on search terms that contain the string “Machine Learning”. What about those terms like “support vector machines” that are still very much related to Machine Learning but don’t explicitly use the search term but, are important terms, that we want to rank for.
Semantic search has meant these type of terms, long tail keywords and related search terms are becoming more and more powerful and important in a modern day SEO strategy.
With semantic search in mind, it’s important to build more meaningful keyword lists or databases that are rich in context and take the searchers intent into account.
How do we build this semantic rich keyword list?
The first thing you need to do is to start thinking of personas. Try and figure out what your target user, who is interested in “Machine Learning” is searching for on Google. Cause they’re not always going to search “Machine Learning”. We’re not just gathering a long list of keywords we want contextually rich keywords that are relevant to our topic.
The second step is to start by identifying what your core topics and concepts are, think of these as the root of all your keywords in your list or database. Move away from “Machine Learning” as a keyword and think of it as a topic.
What we are trying to do is figure out what are the keywords, phrases and search terms that are relevant to our topic and where else should we go to find related concepts other high performing content content relevant to our topic.
Tapping into popular content to source target keywords and concepts that are related to your business, is an excellent way of starting to build your semantic keyword list. In this case, I’ve used a tool called, Buzzsumo to curate relevant and high performing content that’s related to Machine Learning.
Now we have our content, how do we go about mining it for relevant concepts?
Automated Text Analysis and Natural Language Processing can provide tremendous insight when it comes to building keyword lists. They can be used to simply extract keywords and entities to build a simple keyword list based off occurrences, or you can get a bit more advanced and go a level deeper by automatically extracting concepts and topics from the same content.
Semantically Related Keywords
The idea here is, to gather a list of those keywords, that may be very relevant to our overall topic, but may not explicitly mention it. Think about the Support Vector Machines example again.
We can do this by using AYLIEN’s Related Phrases endpoint. This end point gives you a list of words or phrases that are semantically related to you input. You can generate this list by analyzing your initial list of keywords or even better use a handful of the most relevant concepts you grabbed from the curated content links, as we’ve done below.
Our goal in the beginning was to enrich our keyword research process with more context focused and semantically related keywords from which to build a content or SEO strategy on.
Using Text Analysis techniques and focusing on topics and concepts we’ve generated a list of semantically related keywords that we know are highly relevant to our overall topic “Machine Learning” and are based on concepts and not keyword matching. With Google now focused on understanding searches, looking beyond keyword matching being aware of your target Topics, Concepts and related keywords in your SEO and content strategy is sure-fire way of holding your positions or moving up the ladder.