| # | Phrase | Count |
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Understanding which words and phrases appear most often in a body of text is invaluable for content strategy, SEO optimization, data cleaning, and basic natural language analysis. While simple word frequency helps identify repeated keywords, analyzing n-grams (2-grams, 3-grams, etc.) surfaces multi-word phrases that capture topics, named entities, or persistent expressions in your text. The Phrase (n-gram) Frequency Analyzer finds the most common n-grams in any text sample and provides quick export and filtering tools — everything runs locally in your browser for privacy and speed.
N-grams are useful in many scenarios:
1. Choose n wisely: Unigrams (n=1) reveal single-word frequencies — great for stopword removal checks and keyword counts. Bigrams (n=2) and trigrams (n=3) often capture meaningful collocations like "customer service", "delivery time", or "data privacy". Higher n (4–5) can capture repeated full phrases but require more text to produce meaningful counts.
2. Remove stopwords for clarity: Common stopwords (the, and, of) often dominate unigrams and can dilute meaningful phrases. Toggle stopword removal to focus on content-bearing phrases. But if you need exact phrase counts including stopwords (e.g., to detect "in the event"), keep them.
3. Normalize punctuation and case: Decide whether case sensitivity matters. For most analyses, case-insensitive counts produce cleaner results. Removing punctuation prevents mismatches like "data-driven" vs "data driven".
4. Use min-frequency and top-N: Filtering by a minimum frequency avoids noise from single-occurrence phrases. Use Top N to focus on the most actionable phrases.
The tool splits text into tokens (words) using whitespace after optional punctuation removal and lowercasing (unless Case-sensitive is enabled). It then slides an n-length window across the token sequence to build n-gram keys and counts occurrences. Results are sorted by frequency and presented in a simple table for review and export.
This browser-based analyzer is fast for single-page articles, comments batches, and moderate-size corpora. For very large datasets or production NLP pipelines, use specialized libraries (NLTK, spaCy, scikit-learn) or server-side processing. If you need language-aware tokenization (compound words, contractions) or lemmatization/stemming, consider preprocessing with an NLP library prior to n-gram counting.
Phrase-level analysis reveals patterns that single-word frequency cannot. Use this n-gram analyzer to quickly surface common phrases, guide content edits, inform SEO, and produce CSV exports for deeper analysis. Paste your text, adjust options, preview tokens, and analyze — all in seconds and privately in your browser.
An n-gram is a contiguous sequence of n tokens (usually words). A 2-gram (bigram) is two adjacent words; a 3-gram (trigram) is three.
Stopwords can cause many high-frequency but uninformative n-grams. Removing stopwords helps surface content-bearing phrases.
Case-insensitive is usually preferred for aggregated counts. Use case-sensitive when capitalization changes meaning (e.g., "US" vs "us").
It strips punctuation characters before tokenization so that tokens like "data-driven" and "data driven" are treated consistently.
Start with 2 (bigrams) and 3 (trigrams) — they often reveal meaningful phrases without requiring huge text volumes.
Yes — export as CSV including phrase and count. Use Copy CSV to paste into other tools.
No — processing happens locally in your browser; nothing is sent to servers.
Typical articles and comment batches work fine. Very large multi-MB documents might be slow; for those use server-side solutions.
Yes — tokenization is language-agnostic (split on whitespace). Stopword removal uses a basic English list; for other languages, you can disable stopwords or provide preprocessed input.
Counts are exact for the tokenization rules applied. Variations caused by punctuation or casing may be normalized based on your options.
Yes — paste combined text from multiple documents and the tool will aggregate counts across the whole input.
No — this tool does not lemmatize or stem words. For lemmatized n-grams use an NLP pipeline before counting.
They may be below the min frequency threshold or broken by punctuation/stopwords. Adjust options and rerun.
Yes — Phrase (n-gram) Frequency Analyzer is free and requires no registration.
Not in this simple client-side version; copy settings manually or create a short note for replication.