TF-IDF is a primitive approach
The TF-IDF allows you to measure the importance of a document The importance within a corpus, based on a given term. Its capabilities are netherlands telegram data limited, particularly when using synonyms. Indeed, a document considered highly relevant for “baby” may be ignored for the term “infant.”
Google, on the other hand, knows that the words “baby” and “infant” are closely related (they are synonyms). It understands that a page relevant to one is likely relevant to the other, unless there are context clues in the rest of the query that prove otherwise. This is based on co-occurrence as well as the likelihood that they are both used in similar contexts.
Using TF to determine the importance of a term is an imperfect measure
Determining of use in a SERP is an imperfect measure .
If the search intentions of one half of the corpus differ from the other half, the weight of the term (its importance) will be 50%. However, if all the documents in this corpus use a common word, the latter will be considered the most important term regardless of the intention .
So, you’re going to have to choose and focus on a The importance single intention. But the tool will dissuade you from doing so, because only five results use the term. It will tell you that there are only five results out of 10.
The IDF allows us to counterbalance the TF measurement to determine the rarity (the differentiating elements) of a page.
The method is used based on Google SERPs
Semantic tools using TF-IDF generally exploit the first 10 or 20 results of a SERP without studying the reasons why these pages contain these topics, thus raising two biases:
- Pages may owe their “good” positioning to factors other than content, such as netlinking, for example.
- Using a small number of documents significantly affects the quality of the results . These tools do not take into account low-quality content or short texts.
The margin of error is so high that even taking into account the weaknesses of these tools, you will not have the information you need to make informed decisions.
I suggest you save time by using other, more effective tools. It’s important to analyze all content that addresses your topic.
The TF-IDF analysis method and keyword density tools don’t allow this. If you follow their advice, you’ll have as much chance of success as if you had played a tiercé.
The TF-IDF analyzes and groups pages with different objectives
Selecting all the pages that appear among the top Google results creates other problems. You risk including pages that are too general, too specific, or related to a different industry than your own.
Additionally, the TF-IDF does not include search intent.
In other words, if you have quality content that is focused on campanhas de comércio eletrônico do a different search intent, you will be misled.
If you have poor-quality content that’s been well-optimized for off-site SEO, you’ll also be directed down the wrong path. If you’re undecided between multiple goals, the tool won’t be effective either.
In blue, pages with an informational objective, in green pages with a commercial objective and in yellow a transactional objective.
Tools that use the TF-IDF method only consider pages
By limiting themselves to pages, these tools are not aware of your entire website.
Writing a single page on a topic is usually not enough to optimize content. To do this effectively, you’ll need to create additional content that increases your topical relevance and allows for the use of anchor text and internal links.
At SEOQuantum, we created the semantic crawler to help you with this task.
A note that has no meaning
Scoring a page based on its TF-IDF compliance seems like a good idea at first glance. But if you can’t learn anything about the website or page, that information is meaningless and unusable.
Consider that the page with the highest rating may:
- have a different goal than yours
- To have much more or much less authority
- Have multiple goals
- Cover multiple topics
We believe in AI and its valuable help in enriching content, particularly through key concepts. Here, for baby monitors, AI has distinguished three concepts: the device’s functions, the emission of waves, and the transmitter’s distance.
Help, my editor uses TF IDF
Tools that use the TF-IDF method encourage bad habits among writers and SEO experts. They try to build content around inappropriate keywords or add sections that don’t match the search intent.
While this list may provide some inspiration, it’s far from a real solution.
What happens when you create a keyword list using this methodology? The topics and intent of the different terms will vary. The person receiving this list won’t know what to do with it. It’s simply inefficient.
The TF-IDF: the advantages
Despite its ineffectiveness and inaccuracy, there appears to be value in using cn leads this type of approach. This method can, among other things, inspire you or introduce you to a topic you hadn’t considered . But it can also help you realize that you’ve over-optimized your page (too many keywords, etc.).