(Google seo) how to get users to find your site
You may not be familiar with it, but someone is using this algorithm for keyword research. In fact, TF-IDF is an underutilized tool. With TF-IDF, you can learn more about your competitors’ keywords and how to set them up to create high-quality content.
First of all, what is TF-IDF?
Tf-idf (term frequency – Inverse Document Frequency) represents: term frequency – Inverse Document Frequency. It is a common weighted technique in information retrieval and data mining. This is a text analysis technique that Google USES as a ranking factor to indicate the importance of a word or phrase to a document in a corpus.
Tf-idf has two points:
First, it tells us how often a word appears in the document – this is the term frequency section of TF-IDF. Then, use “inverse frequency” to indicate the importance of the term. It reduces frequent words (such as “the” or “a”) and expands more unique words. If some words appear more frequently than others and have little correlation, you can adjust them.
This weighted score tells us the relevance of the keywords, which is more convenient when we apply it to seo.
The Google will find the content we need through state-of-the-art algorithms that actually include tf-idf-like analysis to ensure that the content is relevant to the topic being searched for. Both want to get more coverage through content, increase the flow without being punished Google, or want to obtain high serps ranking, we all need to do a lot of the work associated with Google search engine, TF – the IDF is one of them.
How to conduct TF-IDF analysis? We need something to reveal the semantic associations of words. Tf-idf helps us understand the value of Google on a very good website. Google’s understanding of the exact metrics that drive engagement is a good indicator of how satisfied searchers are with search results (that is, user satisfaction).
Let me give you a simple example. Let’s say I’m Lemmon. We can get words like “lemonade” and “lemonade” from search engines or keyword research tools. This can be seen in the current keyword search volume and keyword relevance is very high. You can find it in the articles you are talking about, or in the high-quality articles that you both agree with and like.
Next, we calculate the tF-IDF value for this word. The data we need to know is that the total number of word frequency documents contains words
Total number of documents. In a passage, there will be some words, such as “yes” and “yes”. These words come up a lot, but they’re not keywords. Such words are “stop words” — stop words that can be filtered out.
Then we find that some words are likely to appear as often as “lemon,” assuming that “vitamin C” and “lemon” appear as often.
A Google search for “lemon” and “vitamin C” yielded 0.831 million and 737 million results, the total number of documents containing the word. Suppose there is an article, 800 words, lemon appears 16 times, then 0.02 is its word frequency. And let’s say that the total number of documents is 10 billion.
How to calculate TF-IDF
Tf-idf (x) = TF(x) * IDF(x)
TF= the number of times a word appears in the article/the total number of words in the article
IDF=log(total number of documents/total number of documents containing the word +1)
Tf-idf (x) = TF(x) * IDF(x) =0.02*2.0791 =0.041582
Tf-idf (x) = TF(x) * IDF(x) =0.02*1.0772 =0.021544
It can be seen that the tF-IDF value of lemon and vitamin C is higher than that of vitamin C. “Lemon” is the key word in the article.
Perhaps the number of occurrences of a word alone is not enough to measure the importance of a keyword. Because the location of keywords also has an impact on the page, the most important information is usually in the first half of the article. But the advantage of TF-IDF is that it is simple and fast, and can give us topics that Google deems important. Often this is related to rankings, and rankings often mean traffic.