How to Optimize Your Content with TF\*IDF

Using content to connect with users is a rapidly changing process - search engines are constantly updating their algorithms, forcing people to rethink their strategies. In the past, keyword density was the most commonly used method for writing content, but these times have long gone. Since Google's Panda Update was introduced in 2011, the quality of website content has become particularly important. Now, we focus on how search engines weigh keywords. You need to use terms in your texts to show search engines that your content is unique and relevant to the user. This is where TF*IDF analysis can help.

How Search Engines Weight Keywords

There are a number of different ways for search engines to weight keywords, but the most common method is through keyword frequency. Does the content really speak to the reader? Is it exactly what they want to read when they click on your page? Does it solve their problem? The more your content makes sense and is relevant to the user, the more weight it carries for the search engine.

Evaluating the content of a website is a challenge for information retrieval, because machines need to understand the real content of a website. That in mind, machines always work in the same way: mathematics! The question is how to mathematically calculate the topic and intent of an article.

How does TFIDF work?

How Does TF*IDF Work?

The TF*IDF feature can be used to find the weight and importance of a single keyword in a certain context. The importance of the keyword increases proportionally to the number of times the word appears in the document, and depending on how often it appears in other documents on the internet: the "corpus".

All keywords found in the content you're writing can be measured via the TF*IDF formula to judge their importance. The formula is based on a logarithm, and gives a score which is used to determine the most important terms in a document. As it's mathematically based, the TF*IDF formula can be used in any language.

The TF*IDF logarithm is a calculation of the "term frequency" and "inverse document frequency":

• TF: Term Frequency**-** this measures how frequently the term is used in a single document. The longer the document, the more likely it is that the term frequency will be high. This is then divided by the total number of terms in the document.

TF = (Number of time the term appears in the document) / (Total number of words in document)

• IDF: Inverse Document Frequency - this measures the importance of the specific term for its relevancy within the corpus. Commonly used terms i.e. stopwords such as "is", "of" and "the" carry less importance, as they are used frequently in all documents within the corpus. The IDF can be calculated as follows:

IDF = (Total number of documents) / (total number of documents containing the keyword)

Have a look here for more detail about how to calculate TF*IDF.

As a website operator, you will know which keywords you want to rank for. TF*IDF is therefore a particularly useful tool to find out which keywords you need to include in your texts in order to be able to rank for your chosen keyword. For example, if you own an online shop, and you want to rank for the keyword "iphone", you can use the TF*IDF formula to find out which terms rank well for "iphone".

How to Use TF*IDF Step by Step

Check out the steps below to find out how to use the TF*IDF feature on Ryte.

1. Getting Started - enter the keyword you want to analyze. You can also select the language, country and region for your desired search so that you can directly appeal to your target group.

1. The results show data from the first page of the Google search results for the keyword you entered above, in this case iphone. The graph shows the most important weighted keywords for the term iphone.

1. If you click on the tab "Detail mode", you get even more information about the most weighted terms, and can see exactly how many times the term was mentioned on how many pages.

1. Now you can compare these results with your competition. Click on the tab "Competition" and you will see which competitors use these terms. Hover your mouse over the circles to see exactly on which website the term was used, and how many times. With the colour code, you can see if the term has a high or low TF*IDF score.

1. The content editor is a useful tool for analyzing a text in real time, or you can copy and paste content that you have already written into the box. The text assistant will show you terms or topics you could use more often, and terms you use too much. It will also give you information regarding your longest word, the number of words, and estimated reading time. You can also add a schema.org tag to give search engines more information about your content.

Conclusion

The TF*IDF feature is a great feature for helping you produce better, unique content, as it helps you discover which terms your texts should contain if you want to rank for a certain keyword. Use the TF*IDF feature to produce better and relevant content for your users, and improve help improve your website's rankings!

Try out the TF*IDF feature on Ryte for FREE