Sci-Kit Word TF-IDF Calculator

Target word

Selected document number

TF method

IDF method

Normalization

Matching option

Case sensitive matching

Documents, one per line

Example Data Table

Document	Sample Text	Target Term	Expected Use
1	sci-kit helps analysts compare terms in text data	sci-kit	Basic text analysis
2	machine learning tools can use tf-idf for ranking	sci-kit	Missing term comparison
3	sci-kit examples include token counts and vector scores	sci-kit	Corpus ranking test

Formula Used

TF-IDF = TF(term, document) × IDF(term, corpus)

Smooth IDF: ln((1 + N) / (1 + DF)) + 1

Plain IDF: ln(N / DF) + 1

Probabilistic IDF: ln((N - DF + 0.5) / (DF + 0.5))

N is the total document count. DF is the number of documents containing the target word. TF depends on the selected term frequency method.

How to Use This Calculator

Enter the target word. The default word is sci-kit.
Paste documents into the large text box. Keep one document per line.
Select the document number you want to inspect.
Choose TF, IDF, and normalization options.
Press Calculate to view the result above the form.
Use CSV or PDF export for reports and records.

Understanding TF-IDF

TF-IDF is a practical score for judging word importance. It links local use with corpus rarity. A term may appear often in one document. That fact alone is not enough. Common words can appear everywhere. TF-IDF lowers those common terms. It raises words that describe a document more clearly.

Why the Sci-Kit Word Matters

The word sci-kit can point to tool names, notes, lessons, or project tags. When you calculate its TF-IDF, you see where it has stronger meaning. A document with many sci-kit mentions may rank high. Yet the score also depends on how many other documents contain the same word. If every document uses sci-kit, the word becomes less special.

Advanced Inputs

This calculator lets you compare several documents at once. Each line acts as one document. You can choose a target document and a target word. The tool counts total tokens, term hits, document frequency, and IDF. It also supports binary TF, raw count TF, log TF, and augmented TF. These methods help match different analysis styles.

Reading the Result

A high TF-IDF score means the selected word is frequent in the chosen document and rare in the full corpus. A low score can mean the word is absent, rare inside that document, or common across many documents. The ranking table helps compare all document lines. Use it to find the strongest document for sci-kit.

Good Text Practice

Clean text improves the score. Remove unrelated notes when needed. Keep each document on a separate line. Use similar document lengths when possible. Very long documents can dominate raw counts. Normalized scores help reduce that effect. Lowercase matching also helps when words appear with different capital letters.

Useful Applications

TF-IDF supports search, tagging, content audits, and basic natural language processing. It can find important terms in reports. It can compare lessons, product pages, research notes, or customer messages. Export results after each run. Then reuse the table in a spreadsheet, report, or review file. This makes later analysis easier and more consistent.

Limits to Remember

TF-IDF is helpful, but it is not meaning by itself. It ignores word order, context, and intent. Treat results as signals. Combine them with reading, labels, and domain judgment carefully before decisions.

FAQs

What does TF-IDF mean?

TF-IDF means term frequency multiplied by inverse document frequency. It shows how important a word is inside one document compared with the full document set.

Can I calculate only the word sci-kit?

Yes. Enter sci-kit as the target word. The calculator will count that exact token and compare its importance across all pasted documents.

Why does a common word get a low score?

A common word appears in many documents. IDF reduces its weight because it is less useful for separating one document from another.

What is smooth IDF?

Smooth IDF adds one to document counts before division. This avoids harsh results and is useful when the corpus is small.

Which TF method should I use?

Use raw count for simple counts. Use relative frequency for length balance. Use log or augmented TF when long documents create unfair dominance.

What does L2 normalization do?

L2 normalization divides the term score by the document vector length. It helps compare documents with different lengths and term volumes.

Can I export the results?

Yes. Use the CSV button for spreadsheet work. Use the PDF button after calculation for a compact report file.

Why is my score zero?

The score is zero when the target word is absent from the selected document. It can also be zero when settings produce no valid term weight.