It seems like I can’t go a day without hearing someone mention content optimization and TF IDF tool in the same breath. Nine times out of ten, it’s because they lack a solid understanding of both concepts.
Often they confuse content optimization with TF IDF and other inexpensive SEO tools. So let’s clear up the confusion with a post that addresses some common questions surrounding TF IDF, in the context of SEO, and content optimization, courtesy of the Question Application in MarketMuse Premium.
TF IDF is a way of representing text as meaningful numbers, also known as vector representation. It was created to solve an information retrieval problem back in the early 1970s, decades before the World Wide Web made its public appearance.
Since that time, it has played a part in natural language processing algorithms used in a variety of situations, including document classification, topic modeling, and stop-word filtering.
There are two components to TF IDF, term frequency and inverse document frequency. Term frequency measures how often a word appears in a document divided by the total words in the document. Inverse document frequency measures a term’s importance. It’s the log of the total number of documents divided by the number of documents containing the term.
TF IDF is the product of those two measurements.
It’s unlikely that TF IDF plays a major role in how the search engine conducts text analysis or retrieves information. Understanding human text is a complex undertaking in which TF-IDF is a bit player in a symphony of algorithms. Webmaster Trends Analyst at Google, John Mueller said as much, here.
TF IDF is frequently hailed as a magic bullet for content optimization. A particular segment of those in the industry believes that Google relies heavily on the algorithm. According to their logic, this algorithm reveals the most important words to use for a search phrase, incorporating them improves relevance and ranking.
A TF IDF tool is one that relies predominantly, if not entirely, on the TF IDF formula for its output. There are many of these tools marketed to SEOs as a cheap way of optimizing content. However, there are many problems with TF IDF tools, which we’ve written about previously.
TF IDF is used in some content optimization tools. But content optimization is not TF IDF.
No, it’s not. However, it is useful in preparing data for machine learning. In this stage, words need to be encoded as numbers for machine learning algorithms to use.
The process of transforming words into numbers is called feature extraction, also known as vectorization. TD IDF is one way of accomplishing this.
No. The lowest value is 0. Both term frequency and inverse document frequency are positive numbers. Since TF IDF equals term frequency multiplied by inverse document frequency, the product cannot be less than 0.
The highest weighting is when a term occurs many times within a small number of documents. The lowest number is a result of the term occurring in practically all documents. Weights that are in between happen when either the term appears fewer times in a document or occurs in many articles.
TF IDF weighting is a statistical measure evaluating the importance of a word to a document within a collection (also known as a corpus). The weight increases according to how frequently a term appears in the document. But that is offset by how often the word appears in the entire corpus. Words that frequently appear in a document aren’t important if they often appear within the whole collection.
Initially designed to solve an information retrieval problem, TF IDF has found use as part of systems and methods for document classification, topic modeling, and stop word filtering. However, by itself, the algorithm often fails when applied to content optimization.
Written by Stephen Jeske