Word Frequency Counter

The tool takes your text, breaks it into individual words, counts how many times each word appears, and ranks them from most to least frequent. The result is a frequency table: a complete inventory of every distinct word alongside its raw count, its frequency as a percentage of total words, and its rank position.

It also calculates summary statistics: total word count, unique word count, vocabulary density (the ratio of unique words to total words), and a frequency distribution showing how many words appear once, twice, three times, and so on. These aggregate numbers characterize the text as a whole.

Filters let you focus the analysis. Toggle stop words on or off to see either the raw linguistic profile or the content profile. Filter by minimum frequency to suppress words that appear only once. Filter by word length to focus on substantive vocabulary and exclude short function words.

Both tools analyze text frequency, but they measure different units. A word frequency counter operates at the single-word level. It tells you that "marketing" appears 23 times, "strategy" appears 15 times, and "conversion" appears 11 times. Each word is counted independently regardless of what words surround it.

An n-gram analyzer operates at the phrase level. It tells you that "content marketing" appears 12 times and "marketing strategy" appears 8 times. Phrases are counted as units, and context is preserved.

Word frequency is the foundation. It reveals the raw vocabulary. N-gram analysis adds context by showing how that vocabulary combines into meaningful phrases. Use word frequency when you need a vocabulary inventory, a keyword density check, or a readability assessment. Use n-gram analysis when you need to understand how concepts are expressed as phrases.

They're complementary tools. Running both on the same text gives you a complete picture: the individual building blocks and the structures they form.

Raw frequency counts become analytical insights when you know what patterns to look for:

• Topic signal strength. The highest-frequency content words are what search engines associate most strongly with your page. If your primary keyword doesn't rank near the top, your content may be drifting toward a subtopic.
• Keyword density verification. The frequency table gives you the exact count and percentage for any word, so you can check whether your target keyword is in the natural 1-2% range or veering into overuse at 5%+.
• Vocabulary richness. The ratio of unique words to total words (type-token ratio) measures how varied your vocabulary is. Higher ratios indicate more diverse vocabulary; lower ratios indicate more repetition.
• Readability indicators. Texts dominated by short, common words are easier to read than texts full of long, rare words. The frequency table reveals the balance.
• Unconscious word habits. Writers have comfort vocabulary they reach for repeatedly. The frequency table exposes these habits objectively. You might discover "leverage" appears eleven times or "robust" has crept into six sentences.

Word frequency analysis scales from single articles to entire content libraries:

• Single article audit. Run the counter and check three things: does your target keyword rank among the top content words? Are any non-target words appearing at unusually high frequency? What's your vocabulary density? Low density in a long article suggests the writing is circling the same ground repeatedly.
• Comparative audit. Run the counter on your article and the top-ranking competitor for the same keyword. Compare which content words appear in their top twenty that don't appear in yours. Those vocabulary gaps may represent subtopics you haven't addressed.
• Site-wide vocabulary audit. Export text from multiple pages and run frequency analysis on the combined corpus. The resulting table shows the overall vocabulary that defines your site's topical identity in aggregate.
• Content consistency check. Run frequency analysis on articles by different authors writing about the same topic. Significant vocabulary differences suggest inconsistent terminology, which can fragment topical signals.

If you've never run a word frequency analysis before, the shape of the results will surprise you. The distribution isn't gradual. It's dramatically skewed. The most frequent word appears far more often than the second, which appears far more often than the third, in a steep curve that flattens into a long tail of words appearing once or twice.

This is Zipf's Law, one of the most reliable observations in linguistics. Named after linguist George Kingsley Zipf, it states that the frequency of a word is inversely proportional to its rank. In English text with stop words included, "the" typically ranks first at around 7% of all words. "Of" ranks second at around 3.5%.

For content analysis, Zipf's Law means two things. First, the vast majority of your unique words appear only once or twice. Don't be alarmed by a frequency table where 60% of words have a count of one. That's normal. Second, the words that appear at high frequency are disproportionately important to the character of your text.

Stop words are the high-frequency function words that provide grammatical structure but carry little topical meaning: "the," "is," "at," "which," "on," and similar words that dominate any frequency table when unfiltered.

• With stop words included: The frequency table shows the raw linguistic profile. Top positions are occupied by function words, and content-carrying words appear further down. This view is useful for linguistic analysis and readability assessment.
• With stop words filtered: The table shows the topical profile. Top positions are occupied by nouns, verbs, and adjectives that carry meaning. This view is useful for keyword analysis, content auditing, and understanding what the text is actually about.

The tool uses a comprehensive English stop word list by default. For specialized content, you might want to focus on the unfiltered view if you need to analyze overall linguistic structure rather than topic-specific vocabulary.

• Drawing conclusions from short texts. Word frequency analysis needs volume. In a 300-word paragraph, most words appear once and the frequency table has no meaningful ranking. Aim for at least 1,000 words for basic analysis and 3,000+ for reliable comparative work.
• Ignoring context. A word appearing thirty times isn't inherently a problem. In a 3,000-word article about "email deliverability," having "email" appear thirty times is natural. Frequency counts need to be interpreted relative to topic, length, and the word's role.
• Treating frequency as prescription. Discovering that a competitor's article uses "automation" eighteen times doesn't mean yours needs the same count. Use their data to identify terminology you might be missing, not to set mechanical targets.
• Forgetting about word forms. "Run," "runs," "running," and "ran" are four separate entries in a basic frequency table. Without lemmatization, your analysis fragments related words into separate counts.
• Analyzing frequency without analyzing absence. What's not in your text can be as informative as what is. If every competing article uses "compliance" and your table shows zero, that absence is a finding worth investigating.