The Actionable Guide to Doing Better Semantic

Keyword Research

Who Am I?

Read Later: http://searchwilderness.com/

The Prevalence of Semantic Search (Unstructured)Search Engines are coming to rely more-and-more on semantic search technology to understand websites and how users search.• As a result SEOs need to better understand how language

and keywords relate to each other in order to do more effective keyword research.

Do semantic keyword research!

What Is Semantic Search?Strings can represent things:

• Search Engines are looking past exact match keyword occurrences on web pages.

• They are learning the meaning behind keywords and examining how they relate to each other conceptually

• The strength of that conceptual connection being scored for relevancy within search queries and on-page.

What is a mammal that has a vertebrate and lives in

water?

+1 Probability

+1 Probability

+1 Probability

Google Hummingbird

What’s up with Hummingbird?

“Hummingbird is paying more attention to each word in a query, ensuring that

the whole query – the whole sentence or conversation or

meaning – is taken into account, rather than particular words. The

goal is that pages matching the meaning do better, rather than pages

matching just a few words.”

Hummingbird improves semantic understanding of search queries AND makes conversational search better, which is important for the future of mobile and voice search.

Hummingbird SummarizedI like Gianluca Fiorelli’s analysis of the theoretical capabilities of a post-Hummingbird Google search:

1.To better understand the intent of a query;2.To broaden the pool of web documents that may answer that query;3.To simplify how it delivers information, because if query A, query B,

and query C substantively mean the same thing, Google doesn't need to propose three different SERPs, but just one;

4.To offer a better search experience, because expanding the query and better understanding the relationships between search entities (also based on direct/indirect personalization elements), Google can now offer results that have a higher probability of satisfying the needs of the user.

5.As a consequence, Google may present better SERPs also in terms of better ads, because in 99% of the cases, verbose queries were not presenting ads in their SERPs before Hummingbird.

Source: http://pshapi.ro/mozingbird

How Can SEOs Optimize for Semantic Search?

1. Make sure our content delights our users Create quality content and use personas

2. Optimize for searcher intent and build topical authority using semantic topic modeling

Understand how users search andhave command of your niche’s

languageNow THIS is

great content.

Build Topical Authority for a SubjectWhen conducting keyword research, optimizing on-page, or creating content, have a deep understanding of your niche’s language:

1. Understand how concepts relate to one another and which keywords pertain to those concepts.

2. Ensure these concepts are well represented.

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

keyword

Optimize for Searcher IntentHave an exceptional understanding of consumer language and the myriad of ways users may search about your niche

1. What are consumers looking for when they are familiar with your niche? • Language used should represent core keywords.

2. What are consumers looking for when they are not familiar with your niche?• Language tends to be more conversational. You

may uncover more related terms when exploring your niche from this perspective.

3. What else do these two groups search for?• These searches may be directly and/or indirectly

related.

Actually doing Semantic Keyword Research…

Social Media Is an Awesome Data Source

Social Media Is an AWESOME Data Sourcefor Semantic Keyword Research1. Social media data helps us expand our collection of

keyword ideas—especially new, breaking keywords.

2. Social media language is inherently conversational and can help us understand how conversation queries may be phrased.

3. We can use it to mimic the language of the customer, which has a secondary CRO benefit.

#Awesome

Secondary CRO Benefit: The Echo EffectWhile you’re at it, use social media language to mimic the language of your consumer. There are several studies that indicate it may help build trust and boost conversions• Study published in the International Journal of Hospitality

Management: Waitresses who verbally mimicked a person’s

order were more likely to receive higher tips.

• Study publish in the Journal of Language and Social Psychology:

Mirroring people’s words can be very important in building likability, safety, rapport, and social cohesion.http://pshapi.ro/echohospitality

http://pshapi.ro/echoinfluence

Once We Collect SERP and Social Media Data...There are some way we can break it down and analyze.

Co-occurrence• How often two or more words appear along side each

other in a corpus of documents.

Latent Dirichlet Allocation (LDA)• Finds semantically related keywords and groups them into

topical buckets.

TF-IDF (Term Frequency-Inverse Document Frequency)• Reflects how important a keyword is to a document in a

whole collection of documents.

The Ultimate Tool

KNIME Is the One Tool to Rule Them All

• Free and open source, running on every platform

• Allows you to do things using a drag-and-drop interface that you would normally need a developer or programming background to accomplish.

• Synergizes data-oriented tasks and helps easily automate: Data collection Data manipulation Analysis Visualization Reporting

http://pshapi.ro/downloadknime

Visualizations KNIME Produces That Will Help Optimize for Semantic Search

Keyword Node GraphsSegmented Word Clouds

Basics of KNIME

What’s a Node?

• Pre-built drag-and-drop boxes designed to do a single task.

• They are combined together into “workflows” to do larger, more complex tasks.

• Nodes can be grouped together into meta-nodes which can be configured in unison.

How Do You Add Nodes and How Do They Connect?How do you add nodes?

How do you connect nodes to one-another?

Configuring Nodes and Running WorkflowsConfiguring Nodes

Running Workflows

OR

Accessing Data from SERP and Twitter + Common Node Configurations We’ll Be Using

Get a Twitter API KeyFill out the forms!• Application “Name”,

“Description”, and “Website” don’t matter for our purposes.

Go to “Keys and Access Tokens” tab and grab:• Consumer Key (API Key)• Consumer Secret (API

Secret)Click “Create my access token” and grab:• Access Token• Access Token Secret

Go to: https://apps.twitter.com/

Accessing Social Data – Twitter API Nodes

Right-Click and “Configure” to input API

information

Right-Click and “Configure” Twitter

Search Query (and type)

It’s Stupid Easy

Extract Only the Links from Twitter

A little trickier than it should be since you have to expand t.co links and URL shorteners.

Accessing SERP Data – Inputting Data Manually

Manually input URLs with Excel Spreadsheet or CSV (Desktop Rank Checkers)

Manually input URLs with “Table Creator” node (Right-Click Configure – edit just like a spreadsheet)

Accessing SERP Data – Inputting Data via API (Better)

Example – GetSTAT

More-Complicated Meta Node Method

Make Webpages Plain Text (for Analysis)

Use Boilerpipe API (pre-made meta-node download to be provided)

http://boilerpipe-web.appspot.com/

Getting Things into a Text Analysis Format

Use the built-in “Strings To Document” node

A Few More Useful Base Nodes for Text Analysis

Parts of Speech Tagging (POS)

Calculate TF-IDF

Co-Occurrence Nodes

LDA (Latent Dirichlet Allocation) Node

Color Manager & Word Cloud

Network Graph

Process: Using KNIME for Semantic Topic Modeling and Keyword Research

Bringing It All Together: Applying Concepts to Visualizations1.Search Twitter for keyword and collect all of the Tweet text2.Search Twitter for keyword, extract links only, scrape text

from links3.Extract top 10 ranking pages keyword and scrape text from

links4.Isolate single word keywords and/or multi-word N-grams5.Calculate TF-IDF

THEN we can…

• Tag Parts of Speech (Nouns, Adjectives, Verbs, etc.) and display in Word Cloud

• Do Co-Occurrence Analysis and display in Node Graph (remember earlier patent?)

• Identify semantic topic groupings with LDA and display in Node Graph

Analysis We Can Do Based on a Google Patent

Simplified with a smaller corpus, but easily replicable with KNIME:

1. Filter out too common terms using TF-IDF2. Take the top 20 or so terms that are above a certain

threshold based upon TF-IDF and remove the rest.3. Calculate Co-occurrence of the remaining terms.4. Optimize your site for these!

Bill Slawski Patent Analysis: http://pshapi.ro/cooccurencepatent

Bringing It All Together – Parts of Speech Output

Bringing It All Together – TF-IDF + Co-Occurence Output

Bringing It All Together – TF-IDF + LDA Output

Now Start Building More Effective semantically Optimized Websites!

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