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@lazarinastoy | @mlforseo
LazarinaStoy.Com
When to use Machine Learning
Models in SEO and which ones to use
trafficthinktank.com
trafficthinktank.com
trafficthinktank.com
How well do you know what your content is about?
clustering and classification
let’s talk about
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Classification sorts data
into specific categories
using a labeled dataset.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Clustering is partitioning
an unlabeled dataset into
groups of similar objects.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Clustering is partitioning
an unlabeled dataset into
groups of similar objects.
We’ll go back to
this in a moment.
How important is classification, really?
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
With Google’s Natural Language API, you can
classify documents in 1,300+ predefined categories
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Process will take no
more than 20 minutes
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Enter your URLs
and content
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Run the script via a formula to
get classification label &
confidence
Plug-and-play template in
Looker Studio
@lazarinastoy | @mlforseo
LazarinaStoy.Com
With Open AI’s GPT4 or ChatGPT, results are a hit or miss.
GPT-4 vs. Google Cloud: Performance comparison on 9 SEO tasks
Do you need content clustering?
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
LDA is great (even though it’s a bit old)
Introduction to Probabilistic Topic Models (Blei, 2012)
Introduction to Probabilistic Topic Models (Blei, 2012)
@lazarinastoy | @mlforseo
LazarinaStoy.Com
LDA emerged to:
● remove dependency on links by introducing the “things” concept and topic/term
understanding
● enable computational understanding of topics and terms and their importance
● highlight that each page will have multiple different topics or subtopics addressed,
which might be of value to different people and should be understood and surfaced
in results
@lazarinastoy | @mlforseo
LazarinaStoy.Com
WATCH THE
DETAILS LATER
I’ve recorded a step-by-step
tutorial on doing topic modelling
using a no-code, publicly-available,
web-based app using LDA.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Identify topics
CRAWL
Export the content
from the website
UPLOAD + FINETUNE
Upload the files to the web
app + finetune
DOWNLOAD
Download all files
EXPLORE + BUILD
Explore the outcome and build
your deliverable
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Identify topics
CRAWL
Export the content
from the website
UPLOAD + FINETUNE
Upload the files to the web
app + finetune
DOWNLOAD
Download all files
EXPLORE + BUILD
Explore the outcome and build
your deliverable
Process will take no more than
30 minutes
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Topic
models
Topic models
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Topic models
Page Info
BERTopic is another great alternative
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Categorise/ discover patterns
and topics on site content
→ Identify opportunities for
internal linking
→ identify what your site is about
and whether it aligns with business
positioning
→ Identify the topics that your
competitor site tackles
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Categorise/ discover patterns
and topics on YouTube titles or
video catalogs
→ Quickly understand competitive
landscape in hundreds or
thousands of videos in a niche
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Categorise/ discover patterns
and topics in first-party data
(any kind of user forms)
→ Quickly see what topics your
feedback is centred upon
Bonus points: Tie this analysis with
sentiment analysis.
Do you really understand what makes up a topic?
keyword clustering
let’s talk about your fancy
software, and how to replace them
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Keyword clustering is
sub-topic keyword extraction
in text-based documents.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
entities
let’s talk about
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Data to analyze
● your content
● competitors’ content
● your SERP data versus competitors’
● first-party data
● UGC
● social mentions
@lazarinastoy | @mlforseo
LazarinaStoy.Com
With Google’s Natural Language API, you can
analyze entities, and entity sentiment.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Process will take no
more than 20 minutes
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Run the script via the
menu to extract
entities from content
Get entity data in
seconds
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Map to topics,
labels or intent
classifications
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Does it matter how you do it?
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Yes, use a tailored model.
53 16
GPT-4 vs. Google Cloud: Performance comparison on 9 SEO tasks
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Entity analysis work is central to multiple SEO projects
(so, pretty important to get it right)
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Do you need to understand text structures, at scale?
let’s talk about
fuzzy matching
@lazarinastoy | @mlforseo
LazarinaStoy.Com
A quick and dirty way for calculating
similarity between two strings
@lazarinastoy | @mlforseo
LazarinaStoy.Com
WATCH THE
DETAILS LATER
I’ve recorded a step-by-step tutorial
on using fuzzy matching for things
like:
● Identifying link opportunities
● String Similarity Analysis
● redirect mapping of URLs
I
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Assesssimilarity
CRAWL + MODEL
Export the content from
the website
ORGANIZE
Теxt to analyze - titles,
URLs, hreflang tags…
ASSESS
Human-check similarity
scores.
ACT
Implement or
recommend
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Language use analysis can also be useful for scaling generation of
or identifying opportunities for Structured Data (schema) mark-up
@lazarinastoy | @mlforseo
LazarinaStoy.Com
@lazarinastoy | @mlforseo
LazarinaStoy.Com
From blog post
Run the cell, paste the content
Download the output JSON
@lazarinastoy | @mlforseo
LazarinaStoy.Com
→ Script tokenizes the text, discovers the
questions, and pulls the answers
@lazarinastoy | @mlforseo
LazarinaStoy.Com
→ Script organises these into a schema
dictionary, which is saved as a JSON file
@lazarinastoy | @mlforseo
LazarinaStoy.Com
What took minutes even with the most
straightforward methods (e.g. using a schema
markup generator, and copy-pasting individual
questions into it), took seconds.
Are recent updates making you rethink content distribution?
Content Transformation
let’s talk about something a bit more advanced (but still beginner-friendly)
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Improve brand omni-presence and content accessibility
Both users and search engines want to see multi-modal presence for high-value sites.
Meaning:
● Text to video
● Videos to text
● Text to audio
● Audio to text
● Text summaries for content distribution on social media
@lazarinastoy | @mlforseo
LazarinaStoy.Com
Needless to say scaling production here is pretty
significant for organic growth.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
You have a library of videos on
YouTube but no blog?
→ Scale it’s launch quickly
through transcription.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
You have a library of
high-performing blog posts
but no presence on
YouTube/TikTok?
→Scale production with
text to speech.
@lazarinastoy | @mlforseo
LazarinaStoy.Com
You have a library of high-performing
blog posts but no content distribution?
→Transform blog posts to insightful
posts for social media.
trafficthinktank.com
half of the people in the room are thinking
trafficthinktank.com
“ I got this.”
trafficthinktank.com
“I could never.”
mindset + community + resources
@lazarinastoy | @mlforseo
LazarinaStoy.Com
trafficthinktank.com

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