Desperate: Keyword Density Checker breaking on-page optimization, data completely off, what's wrong?

Hello Laila Saleh,

it truly feels like the core parsing logic itself is broken, or maybe something with how it fetches and processes the page content.

Given your detailed troubleshooting and the context of a recent backend update, it's highly probable the issue lies within how your tool acquires or interprets the page content before applying its keyword density and frequency algorithms. Let's break down potential causes and a systematic approach to diagnose this. Your existing checks rule out common client-side or basic network issues, which is a good starting point. The lack of obvious server errors (500s) suggests the application isn't crashing outright, but rather failing silently or returning unexpected data. Here are the critical areas to investigate next, focusing on the content acquisition and initial parsing stages:

1. Content Fetching Mechanism:

   • HTTP Client Configuration: Review any changes to your HTTP client (e.g., Guzzle, Axios, Requests in Python) during the refactoring. Headers (User-Agent, Accept-Encoding), timeouts, redirects, or SSL verification settings could be unintentionally altered, leading to failed fetches or incomplete responses.
   • Blocking/Rate Limiting: While you've checked server logs, ensure the target websites aren't actively blocking your tool's requests based on User-Agent, IP, or request frequency. A WAF (Web Application Firewall) or CDN rules could be silently dropping requests or returning empty content without a clear HTTP error code your logs would catch.

2. DOM Parsing and Content Extraction:

   • HTML Structure Changes: Even minor refactoring on your backend might have inadvertently updated a dependency that affects how your parser handles specific HTML structures. If your tool relies on CSS selectors or XPath expressions to extract the main content block, verify these selectors are still valid. Many websites change their HTML structure frequently.
   • JavaScript Rendering: Is your tool designed to wait for client-side JavaScript to render the page content? If it's a simple HTTP fetcher, it will only see the initial HTML. Many modern sites heavily rely on JavaScript for content. If your backend update changed how you interact with a headless browser (e.g., Puppeteer, Playwright, Selenium) or if you introduced one, ensure it's configured correctly, has enough resources, and isn't timing out.
   • Encoding Issues: Confirm that the character encoding (usually UTF-8) is correctly detected and processed throughout the pipeline. Incorrect encoding can lead to garbled text or parsing errors, especially with special characters.

3. Text Pre-processing and Tokenization:

   • Dependency Updates: Did any libraries related to text processing, tokenization, stop word removal, or stemming get updated during your refactoring? A breaking change in a minor version of a natural language processing (NLP) library could fundamentally alter how text is prepared for keyword counting, leading to empty or wildly inaccurate results.
   • Filtering Logic: Review any content filtering logic. Are you inadvertently stripping out too much content (e.g., removing all script tags, style tags, but also accidentally removing paragraphs)?

4. Debugging Strategy:

   • Isolate the Problem: Try to pinpoint exactly where the process breaks.
     1. Can your tool successfully fetch the raw HTML content? Log the entire fetched HTML.
     2. Can it successfully parse the HTML into a DOM-like structure?
     3. Can it extract the 'main' text content from that DOM? Log the extracted raw text.
     4. Is the extracted raw text correct and complete?
     5. Does the keyword counting algorithm then produce valid output from that raw text? Log intermediate steps.
   • Rollback (if feasible): The quickest way to confirm if your backend update is the culprit is to temporarily roll back to the previous working version. If it starts working, you've narrowed down the problem significantly to changes introduced in that update.
   • Unit and Integration Tests: If you don't have them, consider adding specific unit tests for your content parsing, text extraction, and keyword counting functions. Feed them known HTML snippets and expected keyword counts. This helps prevent regressions.
   • Compare with External Tools: As a sanity check, use well-established keyword analysis tools (e.g., Ahrefs, SEMrush, Moz Keyword Explorer) on the same problematic URLs. This helps determine if the issue is with your tool's logic or if there's a broader problem with how the target site's content is rendered or made accessible.

Focusing on how your tool acquires the page's rendered content and then processes that into raw text for its content analysis algorithms is crucial. Even minor refactoring can introduce subtle bugs in complex content extraction and semantic analysis pipelines. Hope this helps your conversions!