Term Frequency–Inverse Document Frequency (TF-IDF)

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Term Frequency–Inverse Document Frequency (TF-IDF) is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. It is calculated by multiplying two different metrics:

• Term frequency (TF): The number of times a word appears in a document.
• Inverse document frequency (IDF): A measure of how common or rare a word is across the entire collection of documents.

The IDF score is calculated by dividing the total number of documents in the collection by the number of documents that contain the word. This means that words that are more common in general will have a lower IDF score, and words that are more rare will have a higher IDF score. The TF-IDF score is then calculated by multiplying the TF and IDF scores. This means that words that appear frequently in a document and are rare in the collection as a whole will have a higher TF-IDF score.

TF-IDF is a widely used metric in information retrieval and Natural Language Processing (NLP). It is used to rank documents in search results, to identify important keywords in documents, and to recommend documents to users.

Here is an example of how TF-IDF can be used:

• Suppose we have a collection of documents about different types of animals. We want to identify the most important keywords in a document about dogs. We can use TF-IDF to do this by calculating the TF-IDF score for each word in the document. The words with the highest TF-IDF scores will be the most important keywords in the document.

• For example, the word "dog" will likely have a high TF-IDF score in a document about dogs, because it will appear frequently in the document and will be rare in the collection as a whole. Other important keywords in the document might include "breed," "training," and "exercise."

TF-IDF is a powerful tool that can be used to extract important information from documents. It is used in a wide variety of applications, including search engines, document classification, and text summarization.

Step-by-step Overview of the TF-IDF Vectorization Process

• Calculate the TF for each word in a document by dividing the number of times the word appears in the document by the total number of words in the document.
• Calculate the IDF for each word by dividing the total number of documents in the corpus by the number of documents that contain the word, and taking the logarithm of the result.
• Multiply the TF and IDF values for each word to get the TF-IDF score.
• Repeat steps 1-3 for all the documents in the corpus to create a TF-IDF vector for each document, where the vector's dimension is equal to the vocabulary words.
• Use the TF-IDF vectors for various tasks, such as similarity calculations using cosine similarity or information retrieval.