An Introduction to TF-IDF: What It Is & How to Use It (2024)

TF-IDF is a statistical method commonly used in information retrieval and natural language processing.

It’s an important concept for understanding how search engines analyze web content and identify key terms that can be associated with search queries.

Here’s what you need to know about it.

What Is Term Frequency-Inverse Document Frequency (TF-IDF)?

Term frequency-inverse document frequency (TF-IDF) measures the importance of a word to a specific document.

It’s the product of two statistics: term frequency (TF) and inverse document frequency (IDF).

Term Frequency (TF)

Term frequency (TF) can be defined as the relative frequency of a term (t) within a document (d).

It’s calculated by dividing the number of times the term occurs in the document (ft,d)by the total number of terms in the document.

Here’s the formula:

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For example, say you have a document containing 10,000 terms. And a specific term appears a total of 25 times in the document.

You’d calculate the term frequency as follows:

TF = 25/10,000 = 0.0025

Inverse Document Frequency (IDF)

Inverse document frequency (IDF) measures the amount of information a term provides.

It’s calculated by dividing the total number of documents (N) by the number of documents that contain the term. Then, taking the logarithm of that quotient.

Here’s the formula:

TF-IDF Formula

To calculate TF-IDF, we need to multiply the values of TF and IDF:

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TF-IDF = 0.00325

The final score shows the relevance of the term, with a higher score denoting higher relevance and a lower score denoting lower relevance.

An Example of How to Calculate TF-IDF

So, how does TF-IDF work in practice?

Simply examining the TF, IDF, and TF-IDF formulas can be a bit overwhelming. Let’s take a look at an actual example.

Let’s say that the term “car” appears 25 times in a document that contains 1,000 words.

We’d calculate the term frequency (TF) as follows:

TF = 25/1,000 = 0.025

Next, let’s say that a collection of related documents contains a total of 15,000 documents.

If 300 documents out of the 15,000 contain the term “car,” we would calculate the inverse document frequency as follows:

How to Use TF-IDF

TF-IDF has a number of applications. It can be used as a weighting factor for:

Information retrieval: Variations of TF-IDF are used as a weighting factor by search engines to help understand the relevance of a page to a user’s search query
Text mining: TF-IDF can help quantify what a document is about, which is a central question in text mining
User modeling: Another application of TF-IDF involves assisting in the creation of models for user behavior and interests, which can then be used by product and content recommendation engines

Use Semrush’s On Page SEO Checker for TF-IDF

Looking to do a bit of TF-IDF analysis for your own website? This is where Semrush’s On Page SEO Checker can help.

You can use it to compare TF-IDF scores between your website content and competing pages.

Here’s how:

Enter your domain on the On Page SEO Checker page and hit the “Get ideas” button.

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The tool will then analyze your website. And present you with a report containing a list of ideas for optimizing your website for search engines.

To see TF-IDF scores for a specific page, visit the “Optimization Ideas” tab.

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Find your desired page in the list, and click the blue button showing the total number of ideas for that page.

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Here, you’ll be presented with a list of ideas for that specific page.

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Click on the “See detailed analysis” link under any of the ideas listed in the report.

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Go to the “Keyword Usage” tab.

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You’ll be able to compare TF-IDF scores in the “TF-IDF” section, as shown below.

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Benefits of Using TF-IDF

Here are the main advantages of TF-IDF:

Easy to calculate: Perhaps the biggest benefit of using TF-IDF is that it’s fairly simple to calculate and can serve as a starting point for more advanced analysis
Identifies important terms: It can help identify important terms in a document, which is very useful for understanding what a document is about
Differentiates between common and rare terms: Since TF-IDF looks at both the number of occurrences of a term in a single document—as well as the number of occurrences of the same term in a collection of documents—it helps to differentiate between common and rare terms
Language-independent: TF-IDF works across all languages and is not limited by the language of a document
Scalable: It’s capable of handling very big datasets containing a large number of documents

Disadvantages of Using TF-IDF

TF-IDF also comes with its set of limitations:

Very rare terms can be problematic: IDF scores can be misleadingly high for very rare terms, making them seem more important than they really are
No understanding of meaning or context: TF-IDF only measures term frequency—it doesn’t understand the meaning behind the terms or the context in which they’re used
Ignores word order: TF-IDF doesn’t care about word order so it can’t comprehend compound nouns or phrases as single-unit terms
Difficulties interpreting synonyms and similar words: Since TF-IDF treats each term independently, it can have difficulties recognizing synonyms and similar words, which can lead to misleading scores

The Evolving Role of TF-IDF in AI and Machine Learning

TF-IDF has numerous applications for artificial intelligence (AI) and machine learning algorithms, including information retrieval, text mining, and more.

It keeps evolving alongside AI, with domain-specific TF-IDF models being developed at the moment. These models take into account the characteristics and nuances of specific industries they’re intended for.

Some examples include TF-IDF models aimed at the healthcare industry, which are capable of analyzing clinical notes and medical records to retrieve valuable information for diagnosing and treating diseases.

TF-IDF is now being combined with transformer machine learning models (which learn context by tracking relationships between terms).

It’s also being utilized along with word embeddings.In this approach, terms are mapped to vectors, and the relationships between them are determined based on the distance in vector space.

In other words, these methods improve text analysis and information retrieval.

Stay on Top of TF-IDF with Semrush

You can stay conscious of your content’s TF-IDF scores and compare them with those of your competitors by using Semrush’s On Page SEO Checker.

Apart from showing TF-IDF scores, the On Page SEO Checker can also help you identify dozens of ways to improve your website’s on-page SEO.

And improve your likelihood of ranking your content higher in search engine results.

This post was updated in 2024. Excerpts from the original article by Christina Sanders may remain.

An Introduction to TF-IDF: What It Is & How to Use It (2024)

FAQs

What is the introduction of TF-IDF? ›

TF-IDF is a statistical method commonly used in information retrieval and natural language processing. It's an important concept for understanding how search engines analyze web content and identify key terms that can be associated with search queries.

Get More Info ›

What is the TF-IDF summary? ›

In a simple language, TF-IDF can be defined as follows: A High weight in TF-IDF is reached by a high term frequency(in the given document) and a low document frequency of the term in the whole collection of documents. TF-IDF algorithm is made of 2 algorithms multiplied together.

Why do you need to know about TF-IDF in our time and how can you use it in complex models? ›

Text Classification: TF-IDF is often used as a feature for text classification tasks such as sentiment analysis, spam detection, and news classification. By weighting the importance of words in a document, the TF-IDF scores can be used to train machine learning models to classify text into different categories.

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What are the steps for calculating TF-IDF? ›

How does TF-IDF work?

Step 1: Calculate the term frequency (TF) The term frequency (TF) is the number of times the word appears in the document. ...
Step 2: Calculate the document frequency (DF): ...
Step 3: Calculate the inverse document frequency (IDF): ...
Step 4: Calculate the TF-IDF score:

Mar 20, 2023

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What is an example of TF-IDF? ›

Example of tf–idf

The calculation of tf–idf for the term "this" is performed as follows: In its raw frequency form, tf is just the frequency of the "this" for each document. In each document, the word "this" appears once; but as the document 2 has more words, its relative frequency is smaller.

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What is the importance of TF-IDF? ›

TF-IDF is a natural language processing (NLP) technique that's used to evaluate the importance of different words in a sentence. It's useful in text classification and for helping a machine learning model read words.

What is the difference between IDF and TF-IDF? ›

The more often a term appears in a document, the higher its TF. This reflects the local importance of the term within the document context. Inverse Document Frequency (IDF): Measures the global importance of a term across the document set. The less often a term appears in all documents, the higher its IDF.

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What are the problems with TF-IDF? ›

TF-IDF computes document similarity directly in the word-count space, which can be slow for large vocabularies. TF-IDF does not consider the semantic meaning of words: it cannot understand the context of the words and derive their meaning. Moreover, it fails to define semantic links between words.

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What is the TF-IDF format? ›

The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if smooth_idf=False ), where n is the total number of documents in the document set and df(t) is the document frequency of t; the ...

What are the applications of TF-IDF? ›

TF-IDF is useful in many natural language processing applications. For example, Search Engines use TF-IDF to rank the relevance of a document for a query. TF-IDF is also employed in text classification, text summarization, and topic modeling.

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Why is TF-IDF better than bag-of-words? ›

Unlike, bag-of-words, tf-idf creates a normalized count where each word count is divided by the number of documents this word appears in. N is the total number of documents. The fraction (N / (# documents in which word w appears)) is known as inverse document frequency.

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How does TF-IDF enhance the relevance of a search result? ›

TF-IDF helps to determine the importance of a particular word or phrase by taking into account not only its frequency within a document, but also the frequency of the word or phrase in the entire collection of documents.

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What is the math behind TF-IDF? ›

It is defined as the product of Term Frequency (number of occurrences of the term in the document) and Inverse Document Frequency. For the document to have a high TF-IDF score (high relevance) it needs to have high term frequency and a high inverse document frequency (i.e. low document frequency) of the term.

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How do I optimize my TF-IDF? ›

Go to Content Analysis > TF-IDF, add or select a page you'd like to analyze, and enter a target keyword. Once the analysis is complete, you get the list of topically relevant terms sorted by the number of competitor pages that use them. You can also choose between the dashboards of multi- and single-word keywords.

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What is one advantage of using TF-IDF over simple word frequency? ›

The main advantage of using TF-IDF over just term frequency (like 'bag of words' model) is that it takes into account not just the isolated usage of a term, but also the term's usage across a set of documents. This can help to give a more accurate measure of the term's importance.

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Who came up with TF-IDF? ›

The concept of TF-IDF was first introduced in the 1970s by researchers Karen Spärck Jones and Stephen Robertson at the University of Cambridge.

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What is the TF-IDF keyword? ›

TF stands for Term frequency, the weight of a term that occurs in a document is simply proportional to the term frequency. IDF stands for "inverse document frequency” or how popular a term is on other sites. WDF is "within document frequency” and describes frequency of a word within a document.

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