TF-IDF Refresher

TF-IDF Refresher

Hard-coding the most popular text-embedding algorithm… We shall look at some basic text-classification processes including text-normalization and feature-extraction which culminates in TF-IDF vectorization.

Term Frequency-Inverse Document Frequency is a numerical statistic that is intended to reflect how important a word is to a document, in a collection or corpus.

Simply put, TF-IDF shows the relative importance of a word or words to a document, given a collection of documents.

Note that before we can do text-classification, the text must be translated into some form of numerical representation, a process known as text-embedding. The resulting numerical representation which is usually in the form of vectors can then be used as input to a wide range of classification models.

TF-IDF is the most popular approach to embed texts into numerical vectors for modeling, information retrieval and text-mining.

Over 83% of text-based recommender systems in digital libraries use TF-IDF… (link)

_Other popular text-embedding algorithms are [Word2vec_](https://en.wikipedia.org/wiki/Word2vec) and Global-Vectors (Glove)

So today, we shall look at some basic _text-classification processes including _text-normalization and feature-extraction which culminates in TF-IDF vectorization.

Then, we shall write very simple python functions to perform TF-IDF.

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