Question

What is the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm used for?

Hint:

High TF + Rare word across documents = High TF-IDF score (important keyword).

Answer 1

Concept: TF-IDF (Term Frequency–Inverse Document Frequency) is a statistical technique used in:

• Natural Language Processing (NLP)
• Information retrieval
• Text mining

It helps determine how important a word is in a document compared to a collection of documents (corpus).
Step 1: Term Frequency (TF).

• Measures how often a word appears in a document.
• Higher frequency → More importance in that document.

\[ TF = \frac{\text{Number of times term appears in document}}{\text{Total number of terms in document}} \]
Step 2: Inverse Document Frequency (IDF).

• Measures how rare a word is across all documents.
• Rare words get higher importance.
• Common words (like "the", "is") get lower weight.

\[ IDF = \log \left( \frac{\text{Total number of documents}}{\text{Number of documents containing the term}} \right) \]
Step 3: TF-IDF Calculation.
\[ TF\text{-}IDF = TF \times IDF \] This gives a score indicating the importance of a word in a document.
Step 4: Purpose of TF-IDF.

• Identifies important keywords in documents.
• Removes common but less meaningful words.
• Converts text into numerical form for machine learning.

Step 5: Applications.

• Search engines (ranking results)
• Document classification
• Chatbots and NLP models
• Spam detection

Conclusion:
TF-IDF is used to evaluate the importance of words in text by combining frequency within a document and rarity across documents, making it a key technique in NLP and text analysis.