Word Frequency Counter
Paste or type your text below to see exactly how many times every single word appears, sorted from most to least frequent with live density metrics.
About the Word Frequency Counter
Quick Answer: The Word Frequency Counter computes word counts and percentage density across your text, sorted by occurrence frequency. Features stopword filtering (common words like the, is, at) to uncover primary thematic keywords.
Every token in your text is tallied and ranked from highest to lowest occurrence. Unlike biased keyword generators, this analyzer defaults to showing every term unfiltered, giving creators and editors a realistic portrait of word distributions, vocabulary variety, and keyword prominence.
- Map-based frequency counting, preventing prototype pollution bugs on words like constructor.
- Contraction-aware tokenizer, preserving single apostrophes in words like don't and isn't.
- Lexical density analytics, tracking the ratio of distinct vocabulary terms against the full passage.
Lexical Density and Vocabulary Richness
In linguistic analysis, lexical density measures the proportion of distinct vocabulary words relative to the total number of words in a document (Type-Token Ratio or TTR). High lexical density indicates concise, information-rich prose with minimal repetition, while lower density reflects conversational dialogue, rhetorical cadence, or keyword repetition.
Keyword Density Benchmarks Across Industries
| Content Category | Target Primary Density | Lexical Diversity Target | Primary Objective |
|---|---|---|---|
| SEO Blog Articles | 1.0% to 2.0% | 40% to 55% | Organic search relevance without triggering keyword stuffing filters |
| Technical Documentation | 2.0% to 3.5% | 30% to 45% | Consistent nomenclature and unambiguous API terminology |
| Academic Theses & Papers | 0.8% to 1.5% | 50% to 65% | Scholarly depth, nuanced phrasing, and broad vocabulary range |
| E-commerce Product Copy | 1.5% to 2.5% | 35% to 50% | Feature prominence, buyer search alignment, and quick scanning |
Why counting with Map prevents JavaScript object prototype bugs
Many simple word counters use plain JavaScript objects (e.g. counts[word] = (counts[word] || 0) + 1). However, JavaScript objects inherit default methods from Object.prototype. If text contains the English word constructor, toString, or valueOf, a plain object returns an inherited function reference rather than a number, producing calculation errors. This tool uses a native Map, which contains zero prototype key collisions.
Tokenization rules for contractions and numerals
The parser uses the regular expression /[a-z0-9]+(?:'[a-z0-9]+)?/g. This pattern captures words, numbers, and single apostrophe contractions (like can't or they're). Numerals such as 2026 or 100 are also tracked so financial and quantitative reports receive thorough frequency breakdowns.
Stable frequency ranking without alphabetical bias
When multiple words share the exact same occurrence tally, they preserve their natural chronological appearance order in the source document. JavaScript modern stable sort algorithm ensures that tied words never shift positions randomly on recalculation.
Zipf's Law in Natural Language Analysis
In quantitative linguistics, Zipf's Law establishes that the frequency of any word is inversely proportional to its rank in the frequency table. For example, the second most common word occurs about half as frequently as the first, and the third most common word occurs about one-third as often. This logarithmic decay curve is observable across all natural human languages.
Applying frequency analysis to SEO and copywriting
Copywriters use word frequency tables to eliminate unintended filler repetition, such as relying too heavily on words like really, very, or just. For search engine optimization, monitoring keyword frequency verifies that core topic phrases appear naturally without triggering keyword stuffing penalties.
Step-by-Step Guide: How to Analyze Word Frequency
- Paste or Type Content: Insert your article, essay, code documentation, or customer reviews into the editor.
- Inspect Live Metrics: Review total word count, unique words, lexical diversity percentage, and the most common word.
- Filter Out Stop Words: Check Exclude common stop words to hide grammatical particles and highlight primary subject matter keywords.
- Search Specific Terms: Use the live search bar to verify how many times a target keyword or phrase appears.
- Export Structured Data: Click Export CSV to download the entire ranked distribution for spreadsheet analysis.
Frequently Asked Questions
Does typing the word constructor or toString break the word counter?
No. Words are counted using a native JavaScript Map rather than a plain JavaScript object. Plain objects inherit properties like constructor and toString from Object.prototype, which can cause key collisions. A Map contains no inherited properties, guaranteeing that every word is counted accurately.
How does the tokenizer handle hyphenated words and contractions?
The tokenizing regular expression matches alphanumeric sequences and allows one apostrophe-linked suffix, accurately preserving common English contractions like do not or does not as single tokens. Hyphenated words are separated into distinct constituent words.
Do digits and numbers appear in the word frequency breakdown?
Yes. The tokenizing pattern includes digits alongside letters, so years, quantities, and numeric identifiers appearing multiple times are counted and ranked alongside vocabulary words.
How are words with identical frequency counts sorted?
Words sharing the exact same frequency count maintain the order of their first appearance in the original text. The sorting algorithm uses a stable count-based comparator without alphabetical re-sorting.
What is lexical density and how does it help improve writing?
Lexical density measures the proportion of unique words relative to the total word count. Higher lexical density reflects rich, varied vocabulary, while lower density indicates repetition. Tracking density helps writers balance keyword prominence with varied phrasing.
Is my text transmitted to any external server during analysis?
No. All tokenization, frequency mapping, and percentage calculations occur strictly within your web browser. None of your text is uploaded, logged, or sent across the network.
What is Zipf's Law and how does it relate to word frequency?
Zipf's Law states that in any natural language corpus, the frequency of any word is inversely proportional to its rank in the frequency table. The most frequent word occurs approximately twice as often as the second most frequent word, and three times as often as the third.
What is the ideal keyword density percentage for SEO content?
For search engine optimization, primary keyword density typically falls between 1% and 2.5%. Densities above 3% risk algorithmic penalties for keyword stuffing, while natural synonym variation enhances search relevance.
How does stopword filtering alter the frequency ranking?
Stopwords are high-frequency grammatical particles such as the, is, at, which, and on. Excluding them allows writers to filter out syntactic noise and immediately identify the dominant topical nouns and verbs.
Can I export the frequency analysis into spreadsheet software like Excel or Google Sheets?
Yes. Clicking Export CSV generates a standard comma-separated file containing Word, Count, and Percentage columns ready for instant import into Microsoft Excel, Google Sheets, or data science environments.