Keyword Extractor
Extract keywords and key phrases from any text using RAKE and TF-IDF algorithms. Ranked by relevance with word frequency analysis — no API, fully client-side.
How to Use
Extract keywords from any text in three steps:
- Paste your text — Enter any article, blog post, essay, or document. The tool works best with at least 100 words. Click a sample button to try a pre-loaded example.
- Choose an algorithm — RAKE extracts multi-word key phrases and scores them by word co-occurrence patterns. TF-IDF extracts individual terms scored by statistical distinctiveness. Toggle between them to see different perspectives on your content.
- Review results — Keywords appear as a weighted tag cloud (larger = higher score) and a ranked list with score bars. Copy all keywords with the clipboard button. Hover any tag to see its exact score and frequency.
About This Tool
RAKE Algorithm
RAKE (Rapid Automatic Keyword Extraction), developed by Rose et al. in 2010, identifies keywords by exploiting the observation that key phrases rarely contain stop words. The algorithm splits text at stop words and punctuation to identify candidate phrases, then scores each word by its "degree" (how many other content words it co-occurs with) divided by its frequency. Multi-word phrases get the sum of their word scores, naturally favoring specific compound terms like "machine learning algorithms" over generic single words.
RAKE is domain-independent — it requires no training data or external corpus. This makes it ideal for extracting keywords from unfamiliar topics. Its main limitation is sensitivity to stop word lists: if a content word appears in the stop list, relevant phrases containing it will be missed.
TF-IDF Scoring
TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure from information retrieval, introduced by Karen Spärck Jones in 1972. Term Frequency (TF) measures how often a word appears in your text. Inverse Document Frequency (IDF) penalizes words that are common across all documents. The product TF × IDF highlights words that are both frequent in your text and rare in general usage — exactly the terms that characterize your content.
This implementation uses an embedded reference list of the 200 most common English words as an IDF approximation. Words not in this common set receive higher IDF scores. For SEO purposes, TF-IDF keywords indicate which terms search engines would associate with your content.
When to Use Each Algorithm
Use RAKE when you want topical phrases — compound terms that capture specific concepts. RAKE excels at academic papers, technical documentation, and content where multi-word terminology is important. Use TF-IDF when you want individual keywords ranked by distinctiveness — ideal for SEO analysis, content tagging, and comparing what makes your text unique. For text analysis, see Word Counter and Language Detector.
Why Use This Tool
Instant Keyword Extraction
Both algorithms run entirely in your browser with zero network latency. There are no API keys, rate limits, or usage quotas. The stop word lists and IDF reference data are embedded in the page code (~2KB), so extraction is instantaneous even for long documents.
Common Use Cases
- SEO content analysis: Extract keywords from your articles to verify they target the right terms. Compare against competitor content to identify keyword gaps.
- Content summarization: Quickly identify the main topics in a long document without reading the entire text.
- Tag generation: Auto-generate tags for blog posts, articles, or knowledge base entries based on extracted keywords.
- Academic research: Identify key terms in research papers for literature review categorization and citation indexing.
- Content comparison: Extract keywords from multiple articles to find topical overlap or unique focus areas.
Privacy
100% client-side processing. Your text is analyzed entirely in your browser. Related tools include Word Counter, Readability Analyzer, Language Detector, and Text Diff.