Inside Text Classification as a Keyword Strategy for Advanced SEO

Inside Text Classification as a Keyword Strategy for Advanced SEO

Choosing where keywords go within content will feel much like arranging furniture.

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.

tf idf

Wikipedia notes variations of the TF-IDF formula. Each variation covers a different frequency or adds a weight to the score. But the overall effect is to factor TF and IDF together to form the TF-IDF score. The magnitude of that score indicates the significant of the keyword’s appearance in the document. If the keyword is common on a site, the TF-IDF will be small (0.02 or so). An infrequent keyword will result in a large TF-IDF value.

Related Article: How to Use Keyword Density in a Modern SEO Strategy

How TF-IDF Benefits SEO

Text classification consists of a variety of techniques, but TF-IDF has seen increased usage in marketing. The advent of digitizing commercial text has opened the technique for applications on website pages, landing pages, social media posts, hashtags and even translated text to identify how frequent a word is being applied across an entire set of text. In fact, Google, along with other search engines, uses a variation of TF-IDF in its algorithm.

For a SEO strategy, TF-IDF gives marketers a broader overview for adjusting keyword placement within a webpage copy or landing page content. As I explained in my post, keyword density places an emphasis on a ratio of words within one page, relying on the analyst’s judgement to make placement decisions. A TF-IDF value accounts for the appearance of a word across documents.

Thus, marketers gain a sense of where a word appears within content. Imagine identifying content gaps among pages, where current keywords may be better placed on another page that can better rank in the top search results. A placement adjustment can prevent keyword cannibalization between similar page content and avoid keyword stuffing on one page.

Applying R Programming to find TF-IDF

If you consider the furniture arrangement analogy, you are using TF-IDF to determine if the keyword relevancy in the pages reflect what you want in a search engine to discovery and include in a query. So where does a marketer begin?

The first step is to gather the words from the content we want to analyze. This can be done several ways with open-source programming languages R programming or Python (for this example, I am using R programming). You can read a text file into the language or use an API to access a software containing the words you want to examine. In the example below, I am using a library called Readtext to read a text file into an object that the program can recognize and consequently analyze.

web content readtext

The object web_content in the example acts as a container, the document part of the TF-IDF, with the actual text appearing in a column associated with the object, text. Here is what that text looks like when it is imported.

tex import

This text is from a website page, used just to work on the example code. Note that it contains a few backslashes or minor character codes. Characters like that sometimes happen when transferring text from one medium to another.

Source link

Leave a Reply

Your email address will not be published. Required fields are marked *