N-gram Analyzer

An n-gram is a contiguous sequence of n items from a text, where each item is typically a word. A unigram is a single word. A bigram is a two-word phrase. A trigram is three words. A four-gram is four. The "n" is just a variable representing how many words are in each chunk.

The concept comes from computational linguistics, where n-gram analysis has been a foundational technique since the 1940s. Claude Shannon used n-gram models to demonstrate information entropy in language. Google built its massive Google Books Ngram Viewer on the same principle to track how word usage has shifted across centuries of published text. Spam filters, autocomplete engines, machine translation systems, and predictive keyboards all rely on n-gram frequency analysis at their core.

For content work, n-gram analysis strips away the narrative and reveals the raw material underneath. You stop reading a piece of writing and start seeing it as a collection of recurring phrases, each with a frequency count that tells you how heavily the author relied on it. That shift in perspective exposes patterns that are invisible during normal reading: the phrases a writer uses compulsively, the terminology that defines a topic's coverage, the language gaps between your content and a competitor's.

The tool processes your text through several stages to produce a ranked frequency table for each n-gram length.

• Tokenization. The text is split into individual words, stripping punctuation and normalizing whitespace. Numbers can be included or excluded. The goal is a clean sequence of words that accurately represents the language used.
• N-gram extraction. The tool slides a window across the word sequence, extracting every consecutive phrase of the specified length. From "the quick brown fox," the bigrams are: "the quick," "quick brown," "brown fox." Every possible consecutive pair is captured.
• Frequency counting. Each extracted n-gram is counted across the entire text. The results are ranked from most frequent to least frequent.
• Stop word filtering. Common function words like "the," "is," "and," "of" dominate unfiltered results. The analyzer offers a stop word filter that removes n-grams composed entirely of these function words, surfacing the content-carrying phrases you actually care about.
• Frequency normalization. Raw counts are supplemented with frequency-per-thousand-words metrics so you can compare n-gram density across texts of different lengths.

Frequency tables are raw data. The value comes from what you do with them. Here are the analytical lenses that turn n-gram counts into actionable insights.

• Topic focus verification. The highest-frequency content n-grams in your text should align with your target topic. If you're writing about "email deliverability" but your top trigrams are dominated by "social media marketing," your content has drifted off topic.
• Keyword density without keyword tools. N-gram frequency is a direct measurement of how often a phrase appears in your text. You can see exactly how many times your target phrase and its variations appear and whether the density looks natural or forced.
• Unintentional repetition. N-gram analysis shows you every recurring phrase ranked by frequency. A phrase you used five times might be fine. The same phrase at fifteen times is a crutch you didn't notice.
• Writing tics and habits. Every writer has go-to constructions they overuse. You might discover that "it's important to" appears in every section opener, or that "in order to" shows up eleven times when "to" would suffice.
• Content comprehensiveness. By comparing n-grams against a reference set, you can identify terminology you haven't used. If every top-ranking article uses "conversion rate optimization" and yours doesn't mention it, that's a coverage gap.

N-gram analysis becomes a competitive intelligence tool when you point it at content that isn't yours. The process reveals how competitors construct their content at the phrase level.

• Extract the linguistic playbook. Paste a competitor's top-ranking article into the analyzer. The resulting frequency table shows you the exact phrases they emphasize.
• Compare coverage across multiple competitors. Run n-gram analysis on three to five top-ranking articles. Look for phrases that appear consistently across all of them. These shared n-grams represent the baseline vocabulary that Google associates with the topic.
• Find differentiation opportunities. The phrases one competitor uses heavily and others don't represent either a unique angle or an idiosyncrasy. You can decide whether to adopt the unique framing or find a third perspective.
• Benchmark before publishing. After writing your draft, use the Compare mode to analyze both your content and a top-ranking competitor side by side. Are your top phrases aligned with the topic? Are you missing terminology?

Each n-gram length reveals different information, and the most useful length depends on what you're trying to learn.

• Unigrams (single words). Show the raw vocabulary of the text. Useful for identifying the dominant topic and checking whether your core keywords appear at appropriate frequency. Limited in analytical value because individual words lack context.
• Bigrams (two words). The sweet spot for most content analysis. Two-word phrases capture meaningful concepts like "content marketing," "search engine," and "user experience." Most target keywords are one to three words, making bigrams the primary window into topical coverage.
• Trigrams (three words). Capture more specific concepts and longer-tail phrases like "search engine optimization" and "conversion rate optimization." Particularly useful for identifying specific subtopics and long-tail keyword opportunities.
• Four-grams and beyond. Capture very specific phrases, sentence fragments, and formulaic expressions. Useful for detecting boilerplate language, repeated sentence templates, and canned phrases.

Start with bigrams for a general overview. Move to trigrams if you need more specificity. Check unigrams for basic vocabulary coverage. Use four-grams to hunt for formulaic patterns.

Semantic SEO is the practice of optimizing content for topical relevance rather than individual keyword matches. N-gram analysis is one of the most direct ways to evaluate and improve semantic coverage.

• Topic modeling from search results. When you extract n-grams from top-ranking content, you're reverse-engineering the term cluster that Google associates with that topic. Including those terms signals comprehensive coverage.
• TF-IDF approximation. N-gram frequency analysis approximates the TF part of TF-IDF. By comparing your frequencies against a reference set, you can identify phrases that are overrepresented or underrepresented in your content.
• Entity and concept coverage. Many entities Google's knowledge graph recognizes are multi-word phrases. "Machine learning," "natural language processing," and "neural network" are bigrams that represent distinct entities.
• Beyond keywords to language patterns. A keyword tool tells you a phrase has search volume. N-gram analysis shows you how those phrases interact with surrounding language in content that actually ranks.

Yes. Use the "Compare Two Texts" tab above. Paste two texts and the analyzer produces frequency tables for each alongside a comparison view showing:

• Shared n-grams. Phrases that appear in both texts, ranked by the difference in frequency between them. This shows where two pieces overlap in vocabulary and where one emphasizes a phrase more heavily.
• Unique n-grams. Phrases that appear in one text but not the other. These are the vocabulary gaps — the concepts one author covered and the other didn't.
• Frequency delta. For shared phrases, the comparison shows the difference in usage. If both articles use "link building" but the competitor uses it three times as often, you can evaluate whether you're under-covering it.

Compare your draft against a competitor's article to check coverage. Compare two versions of your own content to measure how revisions changed emphasis. Compare across authors to identify voice differences.

• Analyzing text that's too short. N-gram frequency analysis needs enough text to produce statistically meaningful patterns. Aim for at least 1,000 words for useful bigram analysis and 2,000 or more for trigrams.
• Leaving stop words unfiltered. Without filtering, your top bigrams will be "of the," "in the," "to the" regardless of the topic. Always filter stop words when analyzing for topic and keyword coverage.
• Treating frequency as a target. Seeing a competitor use "content marketing" 25 times doesn't mean you should too. Use frequency data to understand patterns, not to set mechanical targets.
• Ignoring the difference between frequency and importance. A phrase appearing twelve times might be less important than one appearing twice in the headline. N-gram analysis counts occurrences but doesn't weight by position.
• Analyzing content without context. Comparing n-grams across content types without accounting for differences leads to misleading conclusions. Compare like with like.
• Using n-gram data to justify keyword stuffing. Content that reads naturally at a given density ranks differently from content mechanically padded to hit the same number. Use n-gram data to inform your writing, not to override it.