Term Frequency-Inverse Document Frequency
Explore the TF-IDF technique to transform text into meaningful numerical vectors by balancing term frequency and rarity across documents. Understand its calculation, implementation in Python, advantages over Bag-of-Words, and limitations, preparing you to apply TF-IDF for text representation and machine learning models in natural language processing tasks.
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Introduction
Term frequency-inverse document frequency (TF-IDF) is another text representation technique we use to represent text data before further analysis. In detail, we use this technique to convert the text data we’re working with into numerical vectors, making it suitable for training machine-learning models. Here’s a breakdown of what TF-IDF means:
Term frequency (TF): This measures how often a term (word) appears in a document or text. We calculate it as the ratio of the number of times a term appears in a document to the total number of terms in that document. A higher TF value indicates that a term is important in that document. Here’s the formula for calculating the term frequency, where
represents the term frequency of the specific term, represents the count of how many times the term appears in the document and ...