TF-IDF Keyword Analyzer

TF-IDF stands for Term Frequency-Inverse Document Frequency. It sounds complicated, but the idea is pretty simple - it's a way to measure how important a word is to a document compared to a larger collection of documents.

The "Term Frequency" part looks at how often a word shows up in your content. The "Inverse Document Frequency" part adjusts that score based on how common the word is across all documents. So a word like "the" might appear 50 times in your article, but since it appears everywhere, it gets a low score. A specific term like "content optimization" might only appear 5 times, but since it's more unique, it gets a higher TF-IDF score.

Search engines use algorithms similar to TF-IDF to understand what your content is really about. They're not just counting keywords anymore - they're looking at which terms are significant and relevant to your topic.

When you analyze your content with TF-IDF, you can see which words and phrases carry the most weight. This helps you in a few ways.

• You can identify the key terms that define your content's topic.
• You can spot gaps where you might be missing important related terms.
• You can compare your content to competitors and see what terms they're using that you're not.
• You can avoid keyword stuffing by focusing on terms that actually matter rather than just repeating the same phrase.

Using this tool is straightforward. Here's the process.

1. Paste your article or page content into the first text box.
2. If you want to compare against a competitor, paste their content into the second box. This is optional but helpful for competitive analysis.
3. Choose whether you want to analyze single words, 2-word phrases, or 3-word phrases.
4. Set the minimum count to filter out terms that only appear once or twice.
5. Click "Analyze TF-IDF" and review your results.

The results table shows each term's count, term frequency (TF), and TF-IDF score. Focus on the terms with the highest TF-IDF scores since those are the most distinctive and important for your content.

Keyword density just tells you how often a word appears as a percentage of total words. TF-IDF goes further by considering how common that word is in general.

For example, if you write an article about "coffee brewing methods," the word "coffee" will have high keyword density. But TF-IDF might give a higher score to "pour over" or "French press" because those terms are more specific and distinctive to your topic.

Think of keyword density as a blunt instrument and TF-IDF as a scalpel. Keyword density tells you how much you're using a word. TF-IDF tells you whether that word actually matters for your content's relevance.

TF-IDF scores are relative, so there's no universal "good" number. The scores depend on your content length, the terms being analyzed, and the reference corpus. That said, here are some general guidelines.

• Scores above 0.15 are typically high and indicate important, distinctive terms.
• Scores between 0.05 and 0.15 represent moderately important terms.
• Scores below 0.05 are usually common words or terms that aren't particularly distinctive.

The most useful insight comes from comparing scores within your results. Your top 10-20 terms by TF-IDF score are likely the most important keywords for your content.