Word Frequency Counter & Keyword Density

Word Frequency Counter & Keyword Density

Count word frequency, analyze keyword density, and inspect 1-gram to 3-gram phrases instantly. Filter stop words, detect overused terms, and export CSV reports.

Lbl presets
Title input pane
2.5%
Lbl min length
Lbl custom stopwords
Stat words: 82Stat chars: 631Stat lines: 1Stat sentences: 5
Stat reading time: 25 Unit sec
Metric card total words6782 Lbl raw tokens
Metric card unique words54Lbl distinct words
Metric t t r80.6%Lbl type token ratio
Metric avg length7.2Lbl chars per token
Lbl top occurrences
Lbl target limit ≤ 2.5%
#1keyword
5 Lbl occurrences
7.46%
#2search
5 Lbl occurrences
7.46%
#3density
3 Lbl occurrences
4.48%
#4engine
2 Lbl occurrences
2.99%
#5target
2 Lbl occurrences
2.99%
#6topic
2 Lbl occurrences
2.99%
#7algorithmic
1 Lbl occurrences
1.49%
#8article
1 Lbl occurrences
1.49%
#9authoritative
1 Lbl occurrences
1.49%
#10balanced
1 Lbl occurrences
1.49%
Lbl tone status: Status rich
Lbl engine status
Metric card total words67
Metric card unique words54
Metric card diversity rate80.6%
Technical Reference & Architecture Guide

Word Frequency Counter & Keyword Density Guide

Count word frequency, analyze keyword density, and inspect 1-gram to 3-gram phrases instantly. Filter stop words, detect overused terms, and export CSV reports.

Word Frequency Counter & Keyword Density Guide — Client-Side In-Memory Architecture & Data Flow
Word Frequency Counter & Keyword Density Guide — Client-Side In-Memory Architecture & Data Flow

Key Parameters & Controls

Stopword Filtering Regimenatural language exclusion profile

Excludes high-frequency functional glue words (articles, prepositions, conjunctions, auxiliary verbs) to isolate genuine semantic vocabulary. Standard conversational stopword lists eliminate 150 to 200 grammatical filler terms, which typically account for 40% to 50% of everyday writing volume.

Default: English (175 Stopwords)
N-Gram Collocation Windowphrase sequence grouping

Analyzes continuous multi-word sequences to uncover repeated phrases. A 1-gram tracks individual vocabulary words, a 2-gram highlights two-word pairings like descriptive modifiers, and a 3-gram captures recurrent idioms and common boilerplate expressions across your draft.

Default: 1-Gram (Single Words)
Type-Token Ratio (TTR) Thresholdlexical diversity index

Measures vocabulary variety by dividing unique vocabulary terms by total running word count. Balanced, engaging non-fiction and student essays maintain a ratio between 0.40 and 0.65; scores below 0.35 reveal repetitive phrasing, while scores above 0.75 reflect compact, specialized prose.

Default: 0.50 (Standard Non-Fiction)
SEO Keyword Density Guardrailtarget occurrence percentage

Calculates the exact percentage share a primary focus phrase occupies relative to total text length. The recommended editorial standard spans 1.0% to 2.5%; exceeding 3.0% risks making writing feel robotic and triggers algorithmic penalties for unnatural repetition.

Default: 2.5% Maximum Density

Pro Tips & Best Practices

Zipf's Law Linguistic Baseline

Human speech naturally follows Zipf's Law, where the second most common word occurs roughly half as often as the first, and the third occurs one-third as often. If a meaningful topic word breaks this natural decay curve and crowds into your top three spots alongside common glue words, your article will feel stiff and repetitive to readers.

Type-Token Ratio Length Distortion

Vocabulary diversity scores naturally drop as an article gets longer because everyday connecting words repeat while new concepts level off. When evaluating comprehensive essays over 1,000 words, compare diversity across consistent 400-word sections or use root-adjusted formulas to avoid penalizing in-depth, thorough chapters.

N-Gram Stopword Interleaving Strategy

Never remove helper words before analyzing two-word or three-word phrases, or you will create artificial fragments like 'training marathon pace' instead of 'training for a marathon'. Instead, collect phrase patterns from unaltered text first, then filter out combinations that consist entirely of grammatical filler.

Search Engine Stemming and Topic Clustering

Search crawlers automatically group word variations such as 'healthy snack', 'healthy snacking', and 'healthy snacks' into a single topical entity. Distribute your natural synonyms across the piece rather than repeating the identical phrase, ensuring cumulative topic presence stays comfortably within the 1.5% to 2.5% range.

Common Gotchas & Troubleshooting

⚠️Hyphenated terms and apostrophes splitting into fragmented word pieces

Contractions like 'can't' and compound descriptions like 'plant-based' can be split apart by overly aggressive punctuation strippers. Standard linguistic boundary detection keeps these compounds joined as unified conversational terms so your phrase counts remain true to reader perception.

⚠️Common pronouns and helper verbs crowding out core topic terms

If everyday pronouns (such as 'they', 'our', 'it') dominate your top frequency list, verify that Stopword Filtering is switched on. For recurring conversational filler unique to your project (like 'section', 'click', or 'chapter'), enter them into the custom exclusion list.

⚠️Short drafts under 100 words showing unnaturally high vocabulary diversity

Brief snippets lack enough sample volume for statistical word variety calculations, regularly producing deceptive diversity scores above 0.85. Expand your text sample to at least 250 words to obtain a reliable reflection of your actual writing richness.

Frequently Asked Questions

How to calculate word frequency and keyword density?▼

Keyword density is calculated by dividing the total count of a target word by the total word count of the entire document and multiplying by 100: Density = (Target Word Count / Total Words) * 100%. For example, an article containing 800 words where a focus phrase appears 16 times has an exact keyword density of (16 / 800) * 100 = 2.0%.

What is a good keyword density percentage for SEO?▼

A healthy keyword density for primary search topics ranges between 1.0% and 2.5%. Keeping density within this window signals strong topical relevance without triggering search quality filters, whereas densities surpassing 3.0% to 3.5% risk penalties under Google Search Essentials guidelines for artificial keyword stuffing.

What is the difference between word frequency and n-gram frequency?▼

Word frequency tracks isolated single words regardless of surrounding context, whereas n-gram frequency measures contiguous multi-word sequences. A 1-gram records individual terms, a 2-gram tracks two-word combinations like 'running shoes', and a 3-gram tracks three-word expressions like 'cardiovascular heart rate', pinpointing overused catchphrases and core themes.

What is Type-Token Ratio (TTR) and what is a good score?▼

Type-Token Ratio (TTR) measures lexical variety by dividing the number of unique words by the total word count: TTR = Unique Words / Total Words. For general publications, essays, and educational articles between 400 and 1,000 words, a healthy TTR score falls between 0.45 and 0.65; scores below 0.40 indicate redundant phrasing, while scores above 0.70 demonstrate expressive, diverse vocabulary.

How does Zipf's Law apply to word frequency in writing?▼

Zipf's Law is a universal linguistic rule establishing that word frequency is inversely proportional to its rank in frequency order (frequency is proportional to 1 / rank). In standard English compositions, the most frequent term ('the') represents roughly 7% of all words, the second most frequent ('of') accounts for ~3.5%, and the third ('and') ~2.8%, providing an objective baseline to detect robotic or forced keyword insertion.

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