About Keyword Extractor

Extract keywords and phrases from text content. Part of the DevTools Surf developer suite. Browse more tools in the AI / Modern Dev collection.

Use Cases

• Extract key topics from a research paper or technical document for indexing.
• Identify the main subjects covered in a customer support ticket corpus.
• Find content keywords in a blog draft to inform meta tags and heading structure.
• Analyze competitive content to reverse-engineer the topic coverage strategy.

Tips

• Extract keywords from competitor pages and compare with yours to find topical gaps before writing new content.
• Use multi-word phrase extraction (bigrams, trigrams) in addition to single-word extraction to identify meaningful compound terms.
• Filter out common function words (stopwords) to see only the content-bearing keywords that signal page topics.

Fun Facts

• TF-IDF (Term Frequency–Inverse Document Frequency) was invented by Karen Spärck Jones in 1972 and remains the most widely used statistical measure for keyword extraction — embedded in Elasticsearch, Solr, and most search engines.
• RAKE (Rapid Automatic Keyword Extraction), published in 2010, can extract key phrases from a document in milliseconds without a training corpus by using word co-occurrence and frequency statistics.
• Google's BERT model (2018) shifted keyword analysis from term frequency toward semantic meaning — a page can rank for a keyword that never appears verbatim if the surrounding context is semantically relevant.

FAQ

What algorithm does it use?

TF-IDF for single-document keyword scoring and RAKE for multi-word phrase extraction. Both are configurable; the output shows each keyword's score and frequency.

Does it remove stopwords?

Yes — a built-in English stopword list removes common function words. Custom stopwords can be added; other languages use language-specific lists when the input language is detected.

Can it extract keywords from multiple documents?

Enter up to 5 documents and the tool finds keywords common to all (core topics) and unique to each (differentiating topics) — useful for content gap analysis.