Free Word Counter & Analyzer

How this tool computes its result

Splits input on whitespace runs to count words, counts total and non-space characters directly, counts paragraphs by splitting on blank-line breaks (defaulting to 1 if there is text but no blank-line break), and counts sentences by splitting on runs of . ! ? and filtering empty fragments. Reading time is a flat words-divided-by-200 rounded up to the next whole minute. Keyword frequency strips non-word characters per token, lowercases, filters out a fixed ~50-word stop-word list plus any token 2 characters or shorter, ranks by raw count, and keeps the top 30 for on-screen display (top 20 in the downloadable report). Supplying a target keyword looks up its real count and either updates that keyword's existing row or, if it wasn't already in the top set, inserts it and re-sorts the whole list.

Worked example

Pasting a 200-word paragraph produces "1 min read" (200/200 rounded up equals 1); pasting a 201-word paragraph immediately jumps to "2 min read," since the calculation is a hard ceiling on a flat 200-words-per-minute rate rather than a smoothed average.

When not to use this tool

Sentence counting is a naive split on ./!/?, so abbreviations, decimals, and ellipses all inflate the count -- text like "e.g. the U.S. GDP grew 3.5% in Q1..." reads as several separate sentences rather than one, which also skews the average-words-per-sentence stat derived from that count.

Common mistakes

• - Reading "average characters per word" as excluding punctuation attached to words -- it is computed from total non-space characters divided by word count, so trailing commas and periods still count toward each word's length.
• - Entering a target keyword and expecting it added to the keyword list even if it never appears in the text -- it is only inserted into the displayed frequency list when its real count is greater than zero.
• - Assuming keyword frequency ranks by relevance or SEO value -- it is a flat count of stop-word-filtered token occurrences with no weighting, so a word that is simply long and common in your specific text can outrank a more strategically important but less-repeated term.

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