Placing keywords into website content is like arranging furniture for the home. You must know where the keywords fit comfortably so that users feel at home on your website. Apply text classification to your SEO strategy to help with this. Text classification using Term Frequency/Inverse Document Frequency (TF-IDF) analyzes the importance of words within a given set of words. This will help you better identify what is being emphasized in marketing text and apply important keyword choices into your web content.
What Is TF-IDF and How Is TF-IDF Calculated?
TF-IDF is a text classification score that highlights how each word in a document is relevant. The relevance is based on the number of appearances of that word in the document. This is often used for large research documents like white papers, with demonstrations using words from large novels. The score is a product of two separate calculations. The first calculation is the term frequency, a ratio that examines the keyword count against the overall word count.
The second value is the inverse document frequency, a log scale calculation that compares the keyword against the total words from a document or corpus.
How TF-IDF Benefits SEO
TF-IDF has seen increased usage in marketing due to the digitizing of commercial text. This is quickly becoming the technique for applications on website pages, landing pages, social media posts, hashtags and even translated text. Identifying how frequent a word is being applied across an entire set of text is how Google, along with other search engines, uses a variation of TF-IDF in its algorithm. When developing a SEO strategy, TF-IDF gives an overview for adjusting keyword placement within a webpage copy or landing page content. This can also help when trying to identify content gaps among pages, where current keywords may be better placed on another page that can better rank in the top search results. Placement adjustment can prevent keyword cannibalization between similar page content and avoid keyword stuffing on one page.
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