Ranking higher than your competitors has been deemed of great importance for your small business website. However, it would be important to mention here that not all rank enhancing tools would help you rank higher in the best possible way. As a result, there has been a constant search for the best tool to help you rank higher on popular search engine result pages. A common question with the people in the present time would be about TFIDF usage for SEO. If you wonder what does TFIDF mean for SEO, read on.
Foremost, it would be important to remember that TFIDF should not be deemed as a replacement for a comprehensive optimization strategy. You could make the most of the benefits offered by TFIDF for SEO. However, you would be required to learn to use the TFIDF SEO tool.
Similar to numerous other concepts in SEO, TFIDF has been a much talked about topic. It would be imperative that you read about it as your best bet for ranking your content higher on Google. You might come across statements that TFIDF has become old and not worth the effort. It would be pertinent to mention here that TFIDF has been the best available option made available for SEO.
Understanding TFIDF for SEO
Can you count the number of times a keyword appears in every document? Most people would ignore the size of the documents. Can you compare the count of the keyword to the total number of words? It is called keyword density, which has been a popular content optimization metric of the past.
However, when you rely on keyword density, you may wonder the word ‘to be’ and not the keyword has been the most prominent one in a document. Can you find a way to adjust your calculations for the fact that most words would appear frequently in a speech? You would require using TFIDF. It would help you see how the keyword is used frequently in the document compared to the average use frequency across the other available documents on the internet.
Therefore, you would be able to pay less attention to the other commonly used words. The chances of you distinguishing a specific topic for a particular content would be higher. It would be pertinent to mention here that TF or Term Frequency would be calculated by counting the terms divided by the total word count in the document. Similarly, the IDF or Inverse Document Frequency would be calculated by determining the number of documents divided by the document containing the keyword.
When multiplying by Inverse Document Frequency, the TF or Term Frequency would get lower for commonly used words and relatively higher for unique topic-identifying terms.
A specific word used in every document in English, but only a few documents mentioning the keyword would imply a higher TFIDF for the terms. TFIDF would be used when you require the machine to identify the topics of numerous document sets. For instance, TFUDF would be applicable in recommender systems in huge digital libraries.