An n-gram generator analyzes text by breaking it into overlapping sequences of n words (or characters) and counting their frequency. Whether you are studying writing patterns, building a natural language processing model, or analyzing word usage in documents, this free online n-gram analyzer gives you instant frequency counts for unigrams, bigrams, trigrams, and more. No signup required.
Try the Free Online N-gram Generator
How to Use N-gram Generator — Step by Step
Step 1: Paste your text
Enter or paste any text content — articles, code comments, social media posts, or research papers. The tool handles texts of any length.Step 2: Configure settings
Choose the n-gram size (1-5), whether to normalize to lowercase, and whether to analyze word or character n-grams.
Step 3: Analyze results
Click Analyze to see each n-gram with its frequency count, sorted from most to least common. Copy the results for further analysis.Step 4: Interpret and apply
Review the top n-grams to identify patterns. For writing, reduce repetitive bigrams. For NLP, use as features for models. For SEO, find missing keyword phrases.
Real-World Examples
Writing Analysis: Analyze your essay with bigrams (n=2) to identify common two-word phrases. This helps improve writing style by reducing repetitive patterns.
NLP Research: Generate character trigrams from a corpus to build language models, detect language, or analyze text similarity.
SEO Keyword Analysis: Use bigrams and trigrams on competitor content to discover high-value keyword phrases you might be missing.
Plagiarism Detection (Educator): Compare trigrams between student submissions and known sources. High overlap in 3-gram sequences suggests copied text.
Code Pattern Analysis (Dev): Generate word-level n-grams from a codebase to find repeated boilerplate patterns that could be refactored into functions.
Language Identification (Researcher): Character-level n-grams (n=3,4) can identify the language of unknown text by comparing frequency profiles against known language models.
Features
- N-gram sizes from 1 to 5 (unigram through 5-gram)
- Word-level or character-level n-gram analysis
- Optional lowercase normalization for case-insensitive analysis
- Frequency-sorted output (most common first)
- 100% private — all processing happens in your browser
- Free forever with instant results
- Shows top 50 n-grams by frequency with exact counts
- Handles Unicode text for international language analysis
Tips & Best Practices
- Start with bigrams: Bigrams (n=2) reveal the most actionable phrase patterns for writing improvement and SEO keyword discovery.
- Use character n-grams for language ID: Character trigrams/4-grams work well for language detection and fuzzy matching.
- Normalize for consistency: Enable lowercase for case-insensitive analysis unless case carries meaning (e.g., code analysis).
- Filter stop words first: For cleaner phrase analysis, remove common stop words with Word Frequency before n-gram analysis.
- Compare corpora: Run n-gram analysis on multiple documents and compare top n-grams to find unique vs shared patterns.
- Limit output for readability: The tool shows top 50. For full analysis, copy and filter in a spreadsheet.
- Use for authorship attribution: Character n-grams are effective for identifying writing style fingerprints across documents.
- Combine with TF-IDF: High-frequency n-grams that appear in many documents may be less informative — weight by inverse document frequency.
Common Use Cases
Writers & Content Creators
Identify repetitive phrases and improve writing variety by analyzing your most common word patterns.
NLP Researchers & Data Scientists
Build frequency distributions for language models, text classification, and corpus linguistics analysis.
SEO Specialists
Discover common keyword phrases in top-ranking content to optimize your own content strategy.
Educators & Plagiarism Detection
Compare n-gram overlap between student papers and source materials to detect potential copying.
Code Quality Engineers
Find duplicated code patterns by analyzing token-level n-grams across a codebase.
Social Media Analysts
Analyze trending phrases and hashtags in social media corpora using bigrams and trigrams.
Language Researchers
Study morphological patterns and language evolution through character n-gram distributions.
Frequently Asked Questions
Q: What is an n-gram?
A: An n-gram is a contiguous sequence of n items from text. Unigrams (n=1) are single words, bigrams (n=2) are two-word phrases, trigrams (n=3) are three-word phrases, and so on.
Q: What is n-gram frequency analysis used for?
A: N-gram analysis helps identify common phrases, writing patterns, plagiarism detection, natural language processing, and text mining. It reveals word frequency patterns in documents.
Q: How do I use this tool?
A: Paste your text, select the n-gram size (1-5), and click Analyze. The tool shows each n-gram and its frequency count, sorted by most common first.
Q: Is my text data stored on a server?
A: No. All analysis happens in your browser. Your text is never sent to any server.
Q: Can I use this for plagiarism detection?
A: Yes. N-gram analysis can help identify copied phrases by comparing common patterns across documents.
Q: What's the difference between word and character n-grams?
A: Word n-grams track word sequences (e.g., "machine learning"). Character n-grams track character sequences (e.g., "mach", "achi", "chin" for trigrams). Word n-grams are better for semantic analysis; character n-grams for language detection and fuzzy matching.
Q: Why enable lowercase normalization?
A: Without normalization, "The" and "the" are counted separately. Lowercase merges them for accurate frequency analysis unless case has semantic meaning.
Q: Can I analyze very long texts?
A: Yes. The tool handles texts of any length. For extremely long texts (100K+ words), browser processing may take a few seconds but will complete.
Q: How do I interpret the frequency counts?
A: Higher counts indicate more common patterns. For writing improvement, target n-grams with unusually high counts for diversification. For SEO, target high-count n-grams in competitor content.