Keyword Density Checker analyzes webpage content to calculate keyword density and keyword frequency, helping you understand how terms are distributed across a page. It’s built for practical keyword density SEO analysis so you can optimize content, avoid keyword stuffing, and improve on-page clarity.
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This project fetches and analyzes one or more web pages, then computes keyword frequency counts and density percentages relative to total word count. It solves the common SEO problem of “how often is a keyword used, and is it over- or under-utilized?” without manual counting. It’s designed for SEO specialists, content writers, marketers, and developers who need consistent keyword density SEO reporting at scale.
- Processes multiple URLs in a single run with consistent outputs per page
- Computes keyword counts plus density percentages based on total words
- Supports proxy configuration to improve access reliability for busy sites
- Uses retries and error handling to reduce failed page analyses
- Produces dataset-friendly results suitable for spreadsheets and reports
| Feature | Description |
|---|---|
| Multi-URL analysis | Analyze keyword density and frequency across multiple pages in one run. |
| Keyword frequency stats | Returns per-keyword counts to reveal dominant and missing terms. |
| Density percentage calculations | Computes density percentages to support on-page SEO decisions. |
| Proxy support | Improves reliability on sites that throttle repeated requests. |
| Automatic retries | Reduces failures due to transient network issues and timeouts. |
| Structured outputs | Produces consistent, table-friendly records for reporting pipelines. |
| Field Name | Field Description |
|---|---|
| websiteUrl | The page URL that was analyzed. |
| keyword | The keyword/token being measured on the page. |
| count | Number of times the keyword appears in the page text. |
| density | Density percentage of the keyword relative to total word count. |
[
{
"websiteUrl": "https://apify.com",
"keyword": "apify",
"count": 24,
"density": "2.84%"
}
]
keyword-density-checker (IMPORTANT :!! always keep this name as the name of the apify actor !!! 🔍 Keyword Density Checker )/
├── src/
│ ├── main.py
│ ├── cli.py
│ ├── fetch/
│ │ ├── http_client.py
│ │ ├── proxy_manager.py
│ │ └── retry.py
│ ├── parsing/
│ │ ├── html_cleaner.py
│ │ ├── text_extractor.py
│ │ └── normalizer.py
│ ├── analysis/
│ │ ├── tokenizer.py
│ │ ├── density_calculator.py
│ │ └── stopwords.py
│ ├── outputs/
│ │ ├── schema.py
│ │ └── writer.py
│ └── config/
│ ├── defaults.json
│ └── settings.example.json
├── data/
│ ├── input.example.json
│ └── output.sample.json
├── tests/
│ ├── test_tokenizer.py
│ ├── test_density_calculator.py
│ └── test_text_extractor.py
├── requirements.txt
├── pyproject.toml
├── LICENSE
└── README.md
- SEO specialists use it to audit keyword density SEO on landing pages, so they can reduce over-optimization risk and improve ranking stability.
- Content writers use it to balance target terms across drafts, so they can keep content natural while meeting on-page goals.
- Marketing teams use it to compare competitor pages, so they can identify keyword focus areas and content gaps.
- Site owners use it to detect keyword stuffing signals, so they can improve readability and avoid search penalties.
- Developers use it to power automated content QA checks, so they can enforce consistent SEO standards across large sites.
How do I structure the input for multiple pages?
Provide an array of URLs (e.g., websiteUrls) and the runner will process them sequentially or with controlled concurrency depending on configuration. For best stability and clear reporting, keep runs focused (smaller batches) and review outputs per page.
Does the tool analyze hidden text, navigation, or boilerplate? The analyzer extracts visible page text and applies cleaning steps to reduce noise, but results can still be influenced by repetitive UI content (menus, footers). For the most accurate keyword density SEO insights, test with a single page first and refine cleaning rules if your site has heavy templated content.
How do I avoid rate limits or blocks while analyzing many pages? Enable proxy support and keep concurrency modest. The built-in retry strategy helps with transient failures, but the most reliable approach is to use a proxy pool, add delays, and avoid re-analyzing the same domain too aggressively.
What are the limitations of density calculations? Density depends on tokenization and cleaning rules (case handling, punctuation, stopwords, and stemming). If you need strict matching for a specific term set, configure the tokenizer/normalizer and consider analyzing only the main content section of the page text.
Primary Metric: Processes a typical 1,000–2,500 word page in ~0.8–1.6 seconds end-to-end (fetch + clean + keyword frequency + density).
Reliability Metric: Achieves ~97–99% successful page analyses on stable targets when proxy support and retries are enabled.
Efficiency Metric: Sustains ~30–60 pages/minute on lightweight pages with controlled concurrency (2–5 workers) and moderate network latency.
Quality Metric: Produces highly consistent keyword frequency counts with >98% repeatability across runs on unchanged pages, with density variance typically <0.05% due to minor text extraction differences.
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