# Text Analyzer - Word Frequency, Keyword Density, Readability

> Analyze text in depth: word and character frequency, n-grams, SEO keyword density, Flesch, Fog and SMOG scores, sentence lengths, MTLD and repeated phrases.

- URL: https://qilingtools.com/en/text-analyzer
- Category: Productivity (https://qilingtools.com/en/office)
- Tags: [Text](https://qilingtools.com/en/tags/text), [Productivity](https://qilingtools.com/en/tags/office)
- Last updated: 2026-10-26
- Price: Free, no sign-up
- Privacy: Runs locally in the browser; files are never uploaded

Finished an article, report or landing page and want to know how readable it is, whether your keywords appear often enough, or which phrases you keep repeating? Paste your text or open a txt or md file for analysis that goes well beyond a word count. Words are tokenized with the browser’s Intl.Segmenter, which handles English and Chinese, to build word and character frequency tables plus bigrams and trigrams that never cross sentence boundaries, with optional English and Chinese stopword filters and number exclusion. Enter target keywords to see occurrences, keyword density, where each first appears and whether it’s in the opening line — handy for SEO. English text gets six readability scores: Flesch Reading Ease, Flesch-Kincaid Grade, Gunning Fog, SMOG, Coleman-Liau and the Automated Readability Index; Chinese text gets average sentence length, less-common character ratio, punctuation density and long-sentence share. You also get type-token ratio and MTLD for lexical diversity, a sentence-length histogram, the five longest sentences and a repeated-phrase finder. Export any table, or a full report, as CSV — everything is computed in your browser.

## How to use

1. Paste text or open a txt/md file, and choose whether to filter stopwords
2. Add target keywords, then review frequencies, n-grams, keyword density, readability and sentence lengths
3. Check repeated phrases and the longest sentences, edit your draft and export a CSV report if needed

## Features

- Word and character frequency for English and Chinese via Intl.Segmenter
- Bigrams and trigrams within sentences, with stopword filtering
- SEO keyword density, first position and opening-line check
- Flesch, Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau and ARI
- Chinese metrics: sentence length, less-common characters, punctuation density
- TTR and MTLD lexical diversity, sentence-length chart and longest sentences
- Repeated-phrase finder that drops overlapping fragments
- CSV export per table or as a full report, computed locally

## FAQ

### How should I read the Flesch and grade-level scores?

Flesch Reading Ease runs from 0 to 100; higher is easier, and 60–70 is “plain English” around 8th–9th grade. Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau and ARI estimate the US school grade needed to follow the text, and 7–9 suits a general audience. The formulas rely on sentence length and syllable counts estimated by rules, so an odd word may be off by one syllable; with fewer than 30 words, or under 30 sentences for SMOG, treat the scores as rough.

### What keyword density should I aim for?

Density is the number of words in the keyword times its occurrences, divided by total words. A common guideline is about 1–3% for a main keyword, ideally appearing in the title or first sentence; the table highlights densities that look low or exceed 3%. Search engines reward natural, helpful writing, and keyword stuffing can hurt rankings, so use the number as a sanity check rather than a target.

### What are TTR and MTLD?

Type-token ratio (TTR) is unique words divided by total words; it naturally falls as a text gets longer, so it’s only comparable between texts of similar length. MTLD (Measure of Textual Lexical Diversity) counts how many words it takes, on average, for the running TTR to drop to 0.72, and is largely independent of length — around 70–100 is typical for English prose, and higher means a richer vocabulary.

### How does it find repeated phrases?

It looks for runs of three or more consecutive words that appear at least twice within sentences, then removes any run that’s fully contained in a longer repeated run with the same count, so you see “I love the new design” once instead of every fragment of it. It’s useful for catching verbal tics, boilerplate and copy-paste leftovers.

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