Keyword Extractor

The keyword-extractor library uses a statistical approach to find significant words in text. First, it tokenizes the input into individual words. Then it applies language-specific stopword removal — filtering out common words like "the", "is", "and" in English or "le", "de", "et" in French. The remaining words are the content-carrying terms that define what the text is about. The extraction uses term frequency as a ranking signal: words that appear more often relative to the text length are considered more important. Duplicate removal ensures each keyword appears only once in the output. This approach is fast (pure JavaScript, no ML model needed), lightweight, and effective for identifying the core topics of any text.

SEO professionals extract keywords from their content to verify they are targeting the right terms before publishing. Content marketers analyze competitor articles to identify the topics and keywords they cover. Social media managers convert extracted keywords into hashtags for Instagram, Twitter, and LinkedIn posts. Students identify key concepts in academic papers and textbook chapters. Bloggers check that their articles contain the keywords they intended to rank for. Email marketers verify that subject lines and body copy contain the right trigger words. Researchers tag and categorize large document collections by their core topics. Because this tool runs locally, it is safe for confidential documents, unpublished content, and proprietary text that should never be shared with third-party services.