Looking for keyword research provider

Looking for keyword research provider
I was looking at forum but i can't find service, that provides keyword research... Can you help me with that.
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You can post in the HAF or WTB section.
If you want free keyword research tools, then you can check out Ubersuggest, Keyword Everywhere, and Google Keyword Planner.
I was looking at forum but i can't find service, that provides keyword research... Can you help me with that.
What's your niche?
Do you already have the website?

Perhaps I can help.

Incorrect SEO strategy/unclear objectives.
Poor planning and scoping of resources and timeline.
Unforeseen UX/design changes that impact content or code.
Involving the SEO agency too late/after key decisions have already been set in stone.
Poor or lack of sufficient testing.
Slow responses and low development priority to post-migration bug fixes.
Uncontrollable variables (e.g., Google update).
Poor Strategy

Understanding why the migration is taking place and the desired outcomes is critical in order to set measurable benchmarks for “success.”

For most migrations the objective is to main SEO performance, to then use said stability as a foundation for growth.

However, each migration type has its own set of risks. These need to be communicated to the client and wider stakeholders.

But when relevancy is evaluated (and, most importantly, compared for several articles) by a machine, we need a numeric representation to see that:

Article A is about TF-IDF (as opposed to, say, link building).
Article A is more about TF-IDF than article B.
Could we simply count the number of times our keyword, TF-IDF, appears in each document?

No, thus we obviously ignore the size of the documents.

Could we compare the count of our keyword to the total number of words?

This is what we call keyword density – a widely used content optimization metric of the past.

But relying on keyword density makes me think that the word “to be” (not “TF-IDF”) is the most prominent one in this article.

Is there a way to adjust my calculations for the fact that some words appear more frequently in speech in general?

This is where TF-IDF comes into play, letting us see how “TF-IDF” use frequency in this article compares to its average use frequency across other documents on the Web.

Thus, we’re able to pay less attention to all the commonly used words and distinguish a very specific topic for a particular piece of content.

The formula for my calculations looks like this:

TF-IDF: Can It Really Help Your SEO?

Or, to put it simply (disclaimer: I’m purposefully oversimplifying here for the sake of conveying the basic idea), we’re taking:

Term Frequency = (count of the term) / (total word count in the document)
Inverse Document Frequency = log (number of docs) / (docs containing keyword)
When multiplied by Inverse Document Frequency, Term Frequency gets lower for commonly used words and higher for unique topic-identifying terms.

Back to our example, the verb “to be” is used in each and every article in English. But very few articles mention “TF-IDF”, “keywords”, “content” and other important subtopics I’m covering in my article.

So, TF-IDF for these terms gets higher and… voila! The machine knows what my article is about.

Generally, TF-IDF is used when we need a machine to identify topics of a huge set of documents. For instance, it’s widely applied in recommender systems in digital libraries.

Is Google Using TF-IDF as a Ranking Signal?

The short answer is “no.”

TF-IDF is referred to in a number of Google Patents as something that the search engine may use for stop words removal, which is to get rid of all the function words within a search query and in page content:

TF-IDF: Can It Really Help Your SEO?

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