Text preprocessing

Before we can ask artificial intelligence (AI) to make sense of text, we need to clean it up. Raw text is messy. Spaces, punctuation, slang—it all gets in the way. Preprocessing is just a fancy word for tidying before parsing. Think of it as sweeping the floor before letting guests in.

Tokenization

Start by chopping text into tokens. A token is usually a word, sometimes a symbol. “Cats run fast” becomes [Cats] [run] [fast]. Simple, right? She can’t work without them, because her brain doesn’t read sentences—it reads tokens.

Stopword removal

Some words take up space without saying much. “The,” “is,” “and.” They’re called stopwords. We drop them so she can focus on what matters. If we keep them, she wastes time counting filler. Imagine debugging with half your code full of printf("hello").

Stemming

Words come in families. “Running,” “runner,” “runs.” Stemming chops them back to a root form. “Running” → “run.” It’s crude. The stemmer doesn’t always care about grammar, only trimming. Still, it helps her lump things together faster.

Lemmatization

Lemmatization is like stemming but smarter. It uses dictionaries to find the true base word, the lemma. “Better” becomes “good.” She needs that because not every word family is obvious. Lemmatization takes longer, but the meaning is cleaner.

Our takeaway

We do preprocessing so AI doesn’t drown in noise. Tokens give her eyes. Stopwords clear the clutter. Stemming and lemmatization help her see patterns. Without them, she’s just stumbling through a pile of raw text.

And we’re left wondering: maybe the real debugging isn’t in the code—it’s in the words.