Text N-Grams Generatorv1.0.0

An n-gram is a contiguous sequence of n items extracted from a given text. Word n-grams group consecutive words, while character n-grams group consecutive characters. Frequency analysis of n-grams reveals common phrases, repeated patterns, and structural tendencies within any body of text. The output is a ranked frequency table of all discovered n-grams, showing occurrence counts and optional percentage breakdowns.

• Paste or type your source text into the Text to analyze textarea. The generator accepts any length of plain text, from a single sentence to full documents.
• Select the N-Gram Type. Choose Word to split the text on whitespace and punctuation boundaries, producing sequences of whole words. Choose Character to use a sliding window across the raw text, producing sequences of individual characters including spaces.
• Set the N (number of tokens per n-gram) field to your desired n-gram size. A value of 1 produces unigrams (single words or characters), 2 produces bigrams (pairs), 3 produces trigrams, and so on up to 10.
• Click the Generate button to run the analysis. Results appear below as a frequency table showing each n-gram, its count, and its percentage of the total.
• Open the Settings panel to adjust filtering and display. Set Minimum frequency to filter out n-grams that appear fewer times than the threshold. Set Maximum results to cap the number of rows displayed. Enable Case-sensitive to distinguish between uppercase and lowercase variants. Enable Remove common stop words to exclude articles, prepositions, and other high-frequency function words from word n-grams. Choose between Frequency and Alphabetical sorting. Toggle Show percentages to include or hide the percentage column.
• Fractions (3/4), mixed numbers (5 1/16), decimals (0.75), and scientific notation (1.5e3) are supported in all numeric input fields such as n-gram size, minimum frequency, and maximum results.
• To import previously exported data, click the Import CSV file input and select a .csv file containing n-gram and frequency columns. The imported data populates the results table directly.
• Click the Export CSV button to download the current results as a .csv file containing the n-gram text, frequency count, and percentage for each entry.
• Click the Reset button to clear all inputs, results, and saved settings, restoring every field to its default value.

N-gram frequency analysis applies across many domains where understanding text patterns, word co-occurrences, or character sequences provides actionable insight. Below are practical scenarios illustrating how different fields benefit from n-gram generation.

• Content Writing: Analyze blog posts and articles to identify overused phrases and repetitive bigrams. A writer reviewing a 2000-word article might discover that "in order to" appears 14 times and replace several instances with more direct phrasing.
• SEO Analysis: Extract the most frequent word bigrams and trigrams from top-ranking competitor pages to identify keyword phrases worth targeting. Comparing n-gram frequency tables across multiple pages reveals shared terminology and content gaps.
• Academic Research: Examine a corpus of research papers to find domain-specific collocations and frequently co-occurring technical terms. A linguistics researcher studying a 50,000-word corpus can identify statistically significant bigrams that indicate fixed expressions.
• Plagiarism Detection: Compare trigram frequency tables between two documents to find overlapping three-word sequences. A high overlap percentage between submissions suggests shared source material or insufficient paraphrasing.
• Natural Language Processing: Generate character-level n-grams for language identification or spelling correction models. Character trigram profiles differ between languages, enabling automatic classification of unknown text samples by comparing their trigram distributions against reference profiles.
• Social Media Monitoring: Analyze a collection of tweets or posts mentioning a brand to identify the most common two-word and three-word phrases customers associate with a product. Tracking these n-grams over time reveals shifts in public perception and emerging topics of conversation.
• Cryptography: Apply character bigram and trigram frequency analysis to ciphertext as part of classical cipher-breaking techniques. Comparing observed n-gram frequencies against known language statistics helps identify substitution patterns and narrow the key space.
• Speech and Language Therapy: Analyze transcripts of patient speech to identify word-level bigrams that indicate habitual filler patterns or disfluencies. Tracking the frequency of specific n-grams across sessions measures progress in fluency training programs.