Previously, on Day 3: Explained stopword removal, stemming, and lemmatization in NLP, including how they simplify and normalize text for analysis using practical examples and Python code.
Text Vectorization: Turning Words into Numbers
Computers work with numbers, not text. To handle language, a Natural Language Processing (NLP) system must convert words, sentences, or documents into numerical data. Usually, this means turning them into vectors—ordered arrays of numbers. This process is called text vectorization.
A vector is a mathematical summary of a piece of text. The details and meaning behind the numbers depend on which vectorization method is used, but all serve a common purpose: to translate language into something a machine can process.
For example, imagine building a program to filter spam emails. The program can't directly understand words like "WINNER" or "sale." Every word must be mapped to a number before the program can look for patterns in messages.
