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A token is the smallest unit of text your code works with. Before any analysis, you chop a sentence into these tiny pieces. 🧩

Most of the time a token is just a word. The sentence I love cats becomes three tokens: I, love, and cats.

Punctuation can be its own token. Splitting Hi, there! gives Hi, the comma, there, and the exclamation mark as separate pieces.

Computers cannot read a sentence as one blob. Tokenizing turns messy text into a clean list your program can count and compare.

In Python, tokens usually live in a list of strings. That list is the starting point for almost every NLP task you will build.

Tokens are the unit NLP measures everything in. Word counts, vocabularies, and features all start from this single idea.

A token is not always a full word. Modern models often use subwords, splitting unhappiness into un, happi, and ness to handle rare words.

What about don't? A smart tokenizer may split it into do and n't, because that captures the hidden word not.

Tokens are not only letters. Prices like $5 or years like 2024 are tokens too, and how you split them really matters.

The act of breaking text into tokens is called tokenization. It is the first real step in nearly every NLP pipeline you will write.

There is no single right way to tokenize. The rules you choose shape every count and result downstream, so they deserve real thought.

Let us make sure the core idea stuck.

You learned that a token is the basic unit of text: a word, a symbol, or even a subword. Tokenization splits text into a clean list to process.