Analyze Word & Phrase Frequency Instantly

An n-gram analyzer is one of the fastest ways to understand what a piece of text is actually about. By counting how often individual words and short phrases repeat, it reveals the language patterns, focus keywords, and filler that a quick read can hide. SEOs, content editors, and copywriters use n-gram analysis to check whether a page is on-topic, to find over-used phrases, and to compare their copy against competing pages.

This guide explains what n-grams are, how to read the results, and exactly how to use word, bigram, and trigram frequency data to write tighter, better-targeted content.

What Is an N-Gram?

An n-gram is simply a contiguous sequence of n words pulled from a body of text. The "n" is the length of the sequence:

• Unigram (n = 1) — a single word, e.g. "content".
• Bigram (n = 2) — a two-word phrase, e.g. "content marketing".
• Trigram (n = 3) — a three-word phrase, e.g. "content marketing strategy".

The analyzer slides a window across your text one word at a time, records every n-gram it finds, and then counts how many times each one occurs. The result is a ranked frequency list: the phrases at the top are the ones your text emphasizes most.

Why N-Gram Analysis Matters for SEO and Content

Search engines and large language models both work by recognizing patterns in language. N-gram frequency is a plain-English window into those patterns. A focused, helpful page tends to repeat its core topic phrases naturally; an unfocused page scatters its attention across many unrelated terms.

Practical uses include:

• Topic confirmation — verify your target keyword and its variations actually appear with reasonable frequency.
• Over-optimization checks — spot phrases repeated so often they read as keyword stuffing.
• Filler detection — surface weak, repetitive phrasing ("in order to", "at the end of the day") you can cut.
• Competitor comparison — run a top-ranking page through the analyzer to see which phrases it leans on.
• Brand and entity coverage — confirm important names, products, and entities are present.

How to Read the Results

Counts and percentage share

Each row shows how many times an n-gram occurs (count) and what share of all n-grams of that size it represents (percent). Percentage matters more than raw count when comparing texts of different lengths, because it normalizes for word total.

Stop words

Common words such as "the", "and", "of", and "to" dominate every English text but carry little meaning. Removing stop words (on by default) lifts the meaningful phrases to the top. Turn the filter off when you care about exact phrasing, such as analyzing ad copy or headlines.

Lexical diversity

This metric is the ratio of unique words to total words. A higher number means more varied vocabulary; a very low number can signal repetitive, thin, or templated content.

How to Use the N-Gram Analyzer, Step by Step

• Paste your text — drop in an article, landing page, transcript, or competitor copy.
• Choose the n-gram size — start with bigrams (2 words); they best capture meaningful topic phrases.
• Tune the options — keep stop-word removal on for topic analysis; toggle it off to study exact phrasing.
• Read the top phrases — confirm your target topic leads, and look for anything surprising near the top.
• Export and compare — download the CSV and repeat the analysis on competing pages to spot gaps.

N-Gram Analysis Best Practices

• Compare like with like — analyze full pages against full pages, not a paragraph against an article.
• Use percentages, not raw counts, when texts differ in length.
• Let phrases earn their place naturally; do not write to hit an arbitrary frequency number.
• Pair n-gram data with search intent — the right phrases in the wrong intent still won't rank.