Placing keywords into website content can feel like arranging furniture in a house. Of course, you need a couch and kitchen table set in your house, but the arrangement of that furniture is what makes a house feel like a home. Choosing where keywords go within content will feel much like arranging furniture.
To bring home a sense of great keyword usage for your SEO content strategy, apply a text classification to discover your most important keyword choices. Text classification using Term Frequency/Inverse Document Frequency (TF-IDF) analyzes the importance of words within a given set of words. When applied to web content, marketers can better identify what is being emphasized in their marketing text and adjust.
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. TF-IDF has been used for large research documents like white papers, with demonstrations using words from large novels.
The TF-IDF score is a product of two separate calculations. The first calculation is the term frequency. Term frequency is a ratio that examines the keyword count against the overall word count.
The second value is the inverse document frequency. This is a log scale calculation that compares the keyword against the total words from a document or corpus.
Wikipedia notes variations of the TF-IDF formula. Each variation covers a...
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