AI Keyword Extractor
Extract keywords and key phrases from any text using AI that runs 100% in your browser. Get ranked keywords with relevance scores, named entity recognition, and a visual word cloud. Perfect for SEO, content analysis, and research. Your content is analyzed locally — nothing uploaded.
Word Cloud
Ranked Keywords
| # | Keyword | Score | Type |
|---|
How AI Keyword Extraction Works
This tool combines two powerful techniques to extract the most relevant keywords from your text. First, it uses a TF-IDF-inspired algorithm that analyzes word frequency, position weighting, and n-gram scoring to find statistically significant terms. Then, it enhances these results with Named Entity Recognition (NER) using a BERT model that identifies people, organizations, locations, and other named entities, boosting them in the keyword rankings.
The NER model runs directly in your browser via Transformers.js. Your text is processed locally and never sent to any server. The model is downloaded once (approximately 65MB) and cached for instant offline use.
Extraction Techniques Used
- Term Frequency (TF) — How often a word appears relative to document length
- Position Weighting — Words in the first paragraph and title-like sentences get a boost
- N-gram Analysis — 1-word, 2-word, and 3-word phrases are scored separately
- Stop Word Filtering — Common words like "the", "and", "is" are removed
- Named Entity Boosting — People, organizations, and locations detected by AI get higher scores
Use Cases for Keyword Extraction
SEO and Content Marketing
Identify the primary keywords in your blog posts, landing pages, or competitor content. Compare extracted keywords against your target keywords to ensure alignment. Use the comma-separated output to quickly add keywords to your CMS, meta tags, or SEO tools. The relevance scores help you prioritize which keywords to optimize for.
Academic Research
Quickly extract key concepts from research papers, abstracts, and literature. Identify recurring themes across multiple documents by extracting keywords from each. Use the named entity recognition to find all mentioned researchers, institutions, and locations in a corpus of text.
Content Analysis and Summarization
Get a quick overview of any document by examining its top keywords. Identify the main topics, entities, and themes without reading the full text. This is especially useful for processing large volumes of content such as customer reviews, survey responses, or news articles.
Privacy and Offline Capability
Unlike cloud-based keyword extraction services that upload your text to remote servers, this tool processes everything locally in your browser. The NER model is downloaded once and cached. After that, the tool works completely offline, making it safe for proprietary content, unpublished research, competitive analysis, and any text you want to keep confidential.
Frequently Asked Questions
Is my text kept private during keyword extraction?
Yes, completely. The TF-IDF analysis runs as pure JavaScript and the NER model runs locally in your browser via Transformers.js. Your text is never sent to any server. It is safe for unpublished content, proprietary research, and competitive analysis.
What is the difference between terms, keyphrases, and entities?
Terms are single significant words found by frequency analysis. Keyphrases are 2-3 word combinations that appear multiple times together. Entities are people, organizations, locations, and other named things detected by the AI NER model.
How are keywords scored and ranked?
Keywords are scored using term frequency (TF) with position weighting — words appearing early in the text get a boost. N-grams (multi-word phrases) get an additional multiplier. Named entities detected by AI receive a 2x boost. Scores are normalized to 0-100%.
Does this work offline?
The TF-IDF analysis works immediately without any download. The NER model requires a one-time download (~65MB) that is then cached for offline use. After that, everything works without internet.
Can I use this for SEO keyword research?
Yes. Paste competitor articles, blog posts, or landing page content to identify their primary keywords. Use the comma-separated output to compare against your target keywords. The relevance scores help prioritize which keywords to focus on.
What languages are supported?
The TF-IDF extraction works with any language that uses spaces between words. The NER model is trained primarily on English text and works best with English content. Other Latin-script languages will get partial NER support.