N-gram Generator
Generate and analyze bigrams, trigrams, and custom n-grams from your text to discover patterns and collocations.
Bigram Analysis
Enter some text to see bigram analysis
Understanding N-grams
Bigrams (2-grams)
Consecutive pairs of words that reveal basic relationships and common phrases.
Examples:
- • "machine learning"
- • "data science"
- • "user experience"
Trigrams (3-grams)
Three-word sequences that capture more context and meaningful phrases.
Examples:
- • "natural language processing"
- • "machine learning algorithm"
- • "user interface design"
Higher N-grams
Longer sequences that identify specific phrases and technical terms.
Examples:
- • "artificial intelligence research"
- • "deep learning neural network"
- • "computer vision application"
How to Use the N-gram Generator
Step-by-Step Guide
- Input Text: Paste your content in the text area
- Set Options: Choose case sensitivity and punctuation settings
- Configure Filters: Set minimum frequency and maximum results
- Choose N-gram Size: Select bigrams, trigrams, or custom size
- Sort Results: Order by frequency or alphabetically
- Filter & Search: Use the search box to find specific patterns
- Export Data: Download results as CSV for further analysis
Best Practices
- Text Length: Use at least 100+ words for meaningful results
- Preprocessing: Clean text by removing unwanted formatting
- Frequency Threshold: Start with minimum frequency of 2-3
- Context Matters: Larger n-grams for specific domains
- Compare Results: Analyze different n-gram sizes together
- Domain-Specific: Consider your field's terminology patterns
Common Use Cases
SEO & Content Marketing
Discover natural keyword phrases your audience uses, identify long-tail keywords, optimize content for search engines, and find semantic relationships between terms.
Perfect for: Blog optimization, meta descriptions, keyword research, competitor analysis
Academic & Linguistic Research
Study language patterns, analyze corpus linguistics, research collocations, and examine text structure and style in literary or academic works.
Perfect for: Dissertation research, corpus analysis, language learning, stylistic analysis
Natural Language Processing
Prepare training data for language models, extract features for machine learning, build text prediction systems, and analyze large text corpora.
Perfect for: ML model training, text classification, sentiment analysis, chatbot development
Content Analysis & Strategy
Identify brand messaging patterns, analyze customer feedback themes, extract insights from surveys, and monitor social media conversations.
Perfect for: Brand analysis, customer insights, social listening, content strategy
Frequently Asked Questions
What are N-grams and why are they useful?
N-grams are sequences of consecutive words in text. Bigrams are pairs of words (like 'machine learning'), trigrams are three words ('natural language processing'), and so on. They help identify common phrases, collocations, and patterns in text. N-grams are essential for language analysis, SEO keyword research, content analysis, and natural language processing applications.
What's the difference between bigrams, trigrams, and higher n-grams?
The difference is in the number of consecutive words: Bigrams (2-grams) capture word pairs and show basic word relationships. Trigrams (3-grams) reveal short phrases and better context. Higher n-grams (4+) identify longer phrases and more specific expressions. Generally, larger n-grams are more specific but less frequent, while smaller n-grams are more common but less specific.
How can I use n-grams for SEO and content optimization?
N-grams help identify: keyword phrases your audience uses naturally, semantic relationships between terms, content gaps where you could add relevant phrases, and competitive analysis by analyzing competitor content. Use bigrams and trigrams to find long-tail keywords, optimize meta descriptions, and improve content readability by using natural phrase combinations.
What does the minimum frequency setting do?
Minimum frequency filters out n-grams that appear fewer than the specified number of times. Setting it to 1 shows all n-grams, including those that appear only once. Higher values (2, 3, etc.) focus on more significant patterns by excluding rare combinations. This helps reduce noise and highlight truly common phrases in your text.
Should I include punctuation in my n-gram analysis?
It depends on your goal: Exclude punctuation for general content analysis, keyword research, and when you want clean word-only phrases. Include punctuation for linguistic analysis, when studying writing style, or when punctuation affects meaning (like contractions or hyphenated words). Most content analysis benefits from excluding punctuation.
How do I interpret the coverage percentage?
Coverage shows what percentage of all possible n-grams in your text are represented in the results. High coverage (80%+) means your text has many repeated patterns, suggesting focused content or specialized vocabulary. Low coverage means more diverse language use. This metric helps evaluate text consistency and vocabulary richness.
What are some common applications of n-gram analysis?
N-gram analysis is used for: Content Marketing - finding natural keyword phrases, Academic Research - studying language patterns and corpus linguistics, SEO Optimization - identifying search-friendly phrases, Plagiarism Detection - comparing text similarities, Language Learning - studying collocations and natural expressions, and Machine Learning - training language models and text prediction systems.
How can I export and use the n-gram data?
You can export n-gram results as CSV files containing the phrases, frequencies, percentages, and rankings. Use this data in spreadsheet applications for further analysis, import into SEO tools, create word clouds, build glossaries of key terms, or feed into other text analysis tools. The structured format makes it easy to integrate with other workflows and tools.