What is TFIDF?
TFIDF stands for “Term Frequency-Inverse Document Frequency.” It is a simple math formula that tells you how important a word is in a specific piece of text compared to a whole collection of texts.
Let's break it down
- Term Frequency (TF): counts how many times a word appears in one document; the more it appears, the more “important” it seems there.
- Inverse Document Frequency (IDF): looks at how common the word is across all documents; a word that appears in many documents gets a lower score because it’s less special.
- TF × IDF: multiplies the two numbers, giving a high score only to words that are frequent in one document but rare everywhere else.
Why does it matter?
It helps computers focus on the words that actually tell a story, instead of getting distracted by common words like “the” or “and.” This makes searching, sorting, and understanding text much more accurate.
Where is it used?
- Search engines: ranking web pages that match a user’s query.
- Email spam filters: spotting unusual words that indicate junk mail.
- Recommendation systems: matching product descriptions to what a user likes.
- Academic research tools: finding the most relevant papers on a topic.
Good things about it
- Simple to understand and quick to compute.
- Works well with small to medium sized text collections.
- No need for complex training data or deep learning models.
- Highlights truly distinctive words, improving relevance in search and classification.
Not-so-good things
- Ignores word order and context, so “apple pie” and “pie apple” look the same.
- Can give high scores to rare misspellings or noise words.
- Doesn’t handle synonyms or related concepts without extra processing.
- Performance drops on very large corpora where more sophisticated models may be needed.