Do you know how search engines understand what exactly you are looking for? The answer to this question largely comes down to analyzing the words in the text and their relevance to the search topic.
One important tool in this field is a method called Term Frequency–Inverse Document Frequency, or TF-IDF for short, which plays a prominent role in identifying the keywords and important words of a content.
Search engines use various methods and algorithms to better understand the subject of web pages. TF-IDF, which is widely used in the field of information retrieval and natural language processing (NLP), helps engines like Google determine whether a content contains appropriate keywords and answers related to the user’s question or not.
In this method, the number of times a word is repeated in a content is compared with the repetition of the same word in other content. This comparison helps the search engine better comprehend the main topic of the content.
In this article, we will discuss what TF-IDF is all about and its advantages and disadvantages, so stay tuned until the end.
What is TF-IDF?
TF-IDF stands for Term Frequency-Inverse Document Frequency, which means “the number of times a word occurs in a text – the inverse frequency of the word in all texts.”
This statistical method is used in fields such as natural language processing (NLP) and information retrieval (IR) (such as search engines) to assess the importance of a word in a text (Document) relative to the rest of the text (known as a corpus).
TF-IDF consists of two parts:
- TF or Term Frequency: How many times a word is repeated in the text.
- IDF or Inverse Document Frequency: How many other texts does that word appear in? If it appears a lot, it becomes less important.
By calculating these two numbers, it is determined how valuable a word is in that particular text.
In SEO, TF-IDF has a similar mechanism and is used to identify the topic of the page and help display better results to users. This formula is not just a simple calculation, but also helps to better understand the most important words in a content.
Understanding concept of TF-IDF with a simple example
TF-IDF may seem a bit complicated at first glance, but it can be better understood with a simple example. Suppose you want to write content for your website on the topic “What is TF-IDF?” In this article, you should use keywords such as:
- Number of times a word occurs (TF)
- Keyword density
- Ranking of a word in all texts (IDF)
Because these words are relevant to your topic. In this content, words like “and”, “in” or “is” may be widely repeated, but since they appear a lot in all the content, they do not have any special value.
It is important to understand which words are repeated a lot in this content but are less common in other content on the web. This is exactly what TF-IDF does.
TF-IDF helps you understand which words are more crucial in a particular text; That is, those that appear frequently in your text, but are less common in most other texts.
Practical example of the impact of TF-IDF in content optimization
So, we said that if you want to write about “What is TF-IDF?” and find important words, you should use the weighted keyword frequency method.
For example, to optimize content with the TF-IDF method, it is better to use more main keywords such as “Term Frequency” and “Inverse Document Frequency” so that Google understands that these words are important for your content.
Instead, use related sub-keywords such as “Word Weighting Technique”, “Content Optimization” and “Keyword Extraction” in small numbers to help strengthen the main keywords and better understand the topic by search engines.
Now that we understand what TF-IDF is, let’s see how this criterion is calculated.
What is the TF-IDF calculation formula?
The TF-IDF metric determines the importance of a word by considering the number of times it is repeated in a piece of content and its prevalence in all content. This method is obtained by combining two indicators:
- Term Frequency
- Inverse Document Frequency
What is TF or Term Frequency in TF-IDF?
Word Frequency (TF) is how many times a word is repeated in a piece of content. For example, if you have a 1,000-word piece of content about “content marketing” and the word “content” is repeated 50 times in it, then the frequency of this word is 50 divided by 1,000, which is equal to 0.05.
The TF formula is as follows:
TF (one word) = number of times that word is repeated ÷ total number of words in the text
You must have wondered what the point of counting the number of times a word is repeated in the text is? The fact is that sometimes by examining the number of times a word is used, you can understand whether it is used in the content sufficiently and appropriately.
For example, if you only use the word “content” once in your article, Google may think that this is not very important to you.
On the other hand, if the term is repeated too often, Google may think that you are trying to get a better position in search results by repeating a keyword too much, a method called “Keyword Stuffing.”
What is IDF in TF-IDF?
The Inverse Text Frequency (IDF) is a formula that helps you understand how unique and important a word is. This formula is calculated logarithmically and its general form is:
IDF (word) = logarithm (total number of contents (D) ÷ number of times a phrase is repeated (t))
In fact, IDF determines the value and importance of words and, using logarithms, shows how unique or rare a word is. In this formula, we have two important components:
- D: the total number of contents or pages available, for example, the number of web pages that Google has.
- t: the number of pages where the word in question appears.
If a word is repeated on all pages (such as “and” or “for”), t is large and the value of that word decreases. However, if the word is rare and appears on only a few pages, t is small and the value of that word increases.
For example, if Google has 30 articles and the word “content” is in 25 articles, the IDF value of this word is low, about 0.079, because it is repeated a lot. But if the word “content SEO” is in only 15 articles, its IDF value is higher, 0.301, indicating that this word is more important. In general, common and common words like “for” or “can” have low importance because they are in all the content.
Combining TF and IDF; how to calculate TF-IDF?
Now we come to the interesting part of the story, the TF-IDF method. This method helps you better understand how relevant a word is to a text or content by combining two important criteria: TF and IDF. Its formula is as follows:
TF-IDF = TF(t, d) × IDF(t, D)
In this formula:
- TF(t, d) shows how many times the word “t” is repeated in document “d”.
- IDF(t, D) determines how rare this word is in the entire set of documents “D”.
TF-IDF is one of the old and well-known methods for identifying and ranking relevant content. When a word receives a high weight in the TF-IDF model, it means that the word is repeated a lot in a specific content (high TF) but is seen less in other content (high IDF).
This method helps to eliminate general and unimportant words and focus more on specific and important words for each content. Because of this, TF-IDF can identify words that are truly important in understanding the content of that document.
TF-IDF is an old technique alongside Google’s advanced algorithms
The TF-IDF method is one of the tools that Google uses to analyze, categorize, and understand the content of web pages. “John Mueller,” one of Google’s official representatives, said about it:
“We use different data retrieval methods to understand which words on a page are most important. Over time, various algorithms have been developed.”
That is, simply put, TF-IDF is just one of the tools available, not the whole story. Why? Because:
“It is a relatively old method, and now we have more advanced methods.”
TF-IDF has been around since the early days of Google; but it is likely that the version that Google uses is different from what is in books and articles. Since there is no precise information from within Google, we do not know exactly what algorithm they use.
SEO expert Roger Montti has an interesting comparison:
“In an era where AI and machine learning have become so important, using TF-IDF is like riding a children’s bike next to a Ferrari.”
Therefore, you have to keep in mind that TF-IDF is just a small part of Google’s complex algorithm. The crucial thing is that for SEO, there are much more important factors to focus on.
What is the use of TF-IDF?
TF-IDF is used in various fields such as information retrieval and text analysis. This technique helps to determine the importance of each word in a particular content or document. Below we explain the most important applications of weighted keyword frequency in various fields.
- Text classification and grouping with TF-IDF
TF-IDF can convert text into numbers so that a computer can find similar texts and put them into similar categories. This is used to detect spam emails, categorize news, or organize scientific articles.
- Automatic text summarization with weighted keyword frequency
TF-IDF helps find the most important sentences in a text. By finding important keywords and giving them weight, this method can create a short and useful summary that shows the essence of the story.
- Sentiment analysis
TF-IDF is also used in sentiment analysis. By looking at the TF-IDF score of words, you can understand whether a text has a positive or negative sentiment. For example, in customer reviews, words like “great” that indicate a positive opinion are highlighted with TF-IDF and help understand the overall sentiment of the text.
- Document retrieval systems
TF-IDF plays a key role in systems such as libraries and article databases. By examining the important words in user posts and questions, this method helps to find the best and most relevant content and allows users to easily reach the information they need.
- Topic modeling and text clustering
TF-IDF is a useful method for preparing text before performing more advanced tasks in natural language processing, such as finding topics or categorizing texts.
This method helps to find important words in texts and groups similar texts together based on their topic. In this way, researchers and analysts can more easily find hidden patterns in large text collections.
- Customer service and recommender systems
In the field of customer service, you can use the TF-IDF method to find crucial words and common problems in support messages. This helps improve services and create more useful educational articles.
In addition, recommender systems can analyze user reviews and product descriptions with TF-IDF and recommend appropriate products to users based on interests and keywords.
What is the relationship between SEO and TF-IDF?
The relationship between SEO and TF-IDF is a bit complicated and still evolving. TF-IDF is a method of content analysis that has been used in search engine algorithms for years.
This method is a mathematical formula used to examine content and has been part of Google’s algorithms since the beginning.
John Mueller says you should not just focus on TF-IDF, because it is only a small part of the page ranking method and Google may have a different version of it.
The role of TF-IDF in Google’s algorithm
You can think of TF-IDF as a measure of “keyword density by natural importance.” This method measures how often a keyword is repeated in the text and how natural this repetition is.
Previously, SEOs only looked for a certain percentage of keywords, but Google now has more precise and advanced methods, and these old methods may even result in penalties. TF-IDF is increasingly seen as a positive and helpful factor in ranking websites.
Importance of content quality and user experience vs. TF-IDF
While TF-IDF is still important, Google is focusing more on “content quality” and “user experience.” Consequently, keyword density should only be an auxiliary tool in your overall SEO strategy, not the only solution.
Google is moving away from old-fashioned keyword stuffing and is focusing more on the actual meaning and intent of the user.
Content optimization based on the weighted frequency of keywords
TF-IDF is one of the practical tools for content optimization. Using this method, you can identify important and frequently used keywords and place them correctly in the text to both increase the readability of the content and strengthen its relevance to the topic.
This method helps to write text that is both attractive to the reader and liked by search engines.
Using TF-IDF, you can ensure that the content ranks well in search results and does not lag behind competitors. This method is also effective in finding specific words in each text and helps to organize and enrich the content.
Competitive analysis and identification of related keywords
One of the uses of TF-IDF in SEO is to examine competitors. With the help of this method, SEO experts can examine the content of competing websites and understand which keywords are used most often and which have made them successful.
Tools like DinoRank can compare your content to your competitors’ content and show you which keywords you should use more or less in your text. This will help you develop a better content strategy.
The impact of TF-IDF on search engine results page (SERP) ranking
The correct use of relevant keywords identified by TF-IDF analysis can significantly improve the position of web pages in search engine results. TF-IDF helps search engines better determine the relevance of a page to a user’s search query, which directly increases the visibility and organic traffic of the website.
What is the difference between keyword density and TF-IDF?
TF-IDF is just one of many methods that major search engines like Google use to categorize content.
If you look closely, this method is actually a more advanced version of the same simple concept as “Keyword Density.” Both methods review the importance of words in content.
Keyword density is how many times a keyword is repeated in the text. Tools like Yoast SEO and RankMath calculate this value, but they do so in a simple way, not with complex formulas like logarithms.
- Comparing keyword density to keyword weighted frequency
TF-IDF does not just look at the number of times a word is repeated, but also measures the importance of a word based on how many times it is used in thousands of other pieces of content. This means that if a word appears more frequently in your content but less frequently in other content, that word is considered more important.
With the help of TF-IDF, search engines can better find important and relevant keywords.
This method is more accurate and intelligent, and it makes keywords appear naturally and valuable in the content, rather than just being repeated a lot.
What are the advantages of TF-IDF?
TF-IDF is one of the most widely used and valuable tools in text analysis that has many positive features:
- Identifying important and unique keywords
TF-IDF can find words that are important in a specific content but are not repeated much in other content. This makes specific and specialized words of each content better visible.
- Balancing word frequency and rarity
Unlike simple methods that only pay attention to the number of times words are repeated, TF-IDF also takes into account the rarity of words.
In this way, general words such as conjunctions or auxiliary verbs are given less weight and more specific words are given more importance.
This feature makes TF-IDF perform more accurately than models such as Bag-of-Words.
- Simplicity and understandability of weighted keyword frequency
TF-IDF formulas are simple and understandable. The score that this method gives to each word indicates how important that word is in a piece of content compared to the entire set of content.
Because this method is simple and explainable, it has an advantage over more complex methods such as neural networks. For this reason, TF-IDF is a good choice for starting text analysis projects, basic training, and even comparing with more advanced methods.
- High performance on large data
TF-IDF has the ability to process large volumes of text data. This feature makes it very suitable for applications such as search systems and information retrieval. By intelligently weighting words, TF-IDF helps to better understand text content and increases the accuracy of information retrieval.
- No need to weighted keyword frequency from a specific language
Some of Google’s algorithms still do not work as well in languages such as Persian as they do in English, but the TF-IDF method has a consistent and useful performance in all languages.
Conclusion
TF-IDF is an important statistical method that determines the importance of words in texts. This method better identifies keywords by considering the frequency of a word in a document and its rarity in the entire collection.
In SEO, TF-IDF represents the progress of search engines from focusing solely on the number of keywords to better understanding content, but it is only one small factor in Google’s complex algorithms. The main focus should be on producing organic, high-quality content.
TF-IDF remains a good basic tool and starting point for text analysis, and combining it with new methods gives better results. This method represents an evolution in NLP and SEO; from simple methods to more complex models, but it remains an important reference on the path to progress.
