How to Find Keywords in Text: Proven Extraction Methods

To learn how to find keywords in text, the quickest way is to run the content through an extraction tool that scores terms by statistical importance rather than raw frequency. A tool like the free Topic & Keyword Extractor strips out ordinary words and surfaces the terms doing the actual topical heavy lifting. If you are examining an existing web page that already ranks, you can also check real query data directly inside Google Search Console.

Which keyword extraction method should you use?

The right method depends on what you have in front of you: raw draft copy, a published URL, or an export of search queries. Raw frequency counters fail because common English words like "get", "use", or "time" appear constantly without telling you what an article covers.

• For unpublished drafts or competitor copy: Use a term-weighting extraction tool or an n-gram parser to isolate distinct topical terms and phrases.
• For existing pages on your site: Use Google Search Console to see the exact queries searchers used when Google served your page.
• For quick checks of specific words: Use a text editor or browser search command.
• For large batches of raw text: Use a spreadsheet formula to parse phrase frequency.

Method 1: Extract scored terms using TF-IDF

The most reliable way to find keywords in a text without getting buried in stop words is TF-IDF (term frequency times inverse document frequency). This approach measures how often a word shows up in your specific text weighted against how unusual that word is across ordinary English. Common words score low no matter how often they appear.

You can run this directly in your browser using the Topic & Keyword Extractor:

1. Copy your draft text or page HTML to your clipboard.
2. Paste the text into the extractor input box.
3. Review the ranked output. Tags are stripped, style and script blocks are removed, and headings and lead paragraphs are automatically weighted.
4. Check the extracted phrases alongside single words. If a central concept uses ordinary words, the phrase list is where you will see it in its proper context.

Keep in mind that scores are normalised against the top term inside each run, so the primary term always scores 100. If you compare two pieces of text, look at the ordering of terms rather than comparing the numbers directly. If your draft is heavily marked up with broken HTML, paste the rendered plain text instead so parsing remains accurate.

Method 2: Check queries in Google Search Console

If your text is already published on a live site, the search engine itself tells you which keywords it associates with the page. You do not need to guess how algorithms interpret the text when you have access to real impression logs.

1. Log in to Google Search Console and select your verified property.
2. Click on Search results under the Performance menu on the left sidebar.
3. Click the + New filter button at the top, select Page, choose Exact URL, and enter your page URL (such as https://example.com/blog/seo-guide).
4. Scroll down to the table below the graph and select the Queries tab.
5. Sort by impressions to see which keywords Google considers your text relevant for, and note high-impression queries that have low click-through rates.

Once you extract these queries, you can group related concepts together using a Keyword Clustering Tool to see if your content needs dedicated sections or secondary supporting pages.

Method 3: Break content into phrases using an n-gram analyzer

Single words often lack meaning in SEO. For example, "engine" could mean automotive mechanics, search algorithms, or game development. Multi-word sequences (n-grams) reveal genuine intent.

1. Copy your plain text into an N-gram Analyzer.
2. Select bi-grams (two-word phrases) or tri-grams (three-word phrases).
3. Filter out standard stop words (prepositions, conjunctions, and articles).
4. Review the most frequent noun and verb combinations to determine the true subject matter of the copy.

To double-check the raw density of these core phrases against your total word count, cross-reference them in a Keyword Density Checker to make sure a single phrase is not repeating to an unnatural degree.

Method 4: Parse word counts using a spreadsheet

If you prefer to work offline or process text inside Google Sheets or Microsoft Excel, you can extract unique words and count their frequency using basic spreadsheet functions.

1. Paste your block of text into cell A1.
2. Split the text into individual words across a column. In Google Sheets, enter: =TRANSPOSE(SPLIT(LOWER(REGEXREPLACE(A1, "[^a-zA-Z0-9 ]", "")), " ")) in cell B1.
3. Generate a list of unique words in cell C1 using: =UNIQUE(FILTER(B1:B, B1:B<>"")).
4. Count the occurrences of each unique word in cell D1 using: =ARRAYFORMULA(COUNTIF(B1:B, FILTER(C1:C, C1:C<>""))).
5. Sort columns C and D by column D in descending order to see the most frequent terms at the top.

The limitation here is that stop words like "the", "and", and "of" will sit at the top of your list. You will need to manually remove common words to isolate meaningful terms.

Method 5: Locate known terms using text editor search

When you already know which keyword you want to evaluate and need to verify its placement throughout a document, built-in search tools are the fastest option.

1. Open your text in any editor (such as VS Code, Notepad, Google Docs) or directly in a web browser.
2. Press Ctrl + F (Windows) or Cmd + F (macOS) to open the find interface.
3. Type your target term to view the total match count and review each occurrence in context.
4. Check critical locations: verify whether the term appears in your title, within the first 100 words, and inside an <h2> heading element.

Common mistakes when finding keywords in text

The most common error is relying purely on raw word counts. In any standard 1,500-word English article, terms like "is", "for", and "with" will appear 30 to 80 times each. Counting raw frequency without a stop-word list or inverse frequency weighting leaves you with conversational noise instead of keywords.

Another issue is confusing TF-IDF with Google's ranking systems. TF-IDF is an information retrieval weighting method developed decades ago. Modern search engines do not rank pages based on TF-IDF scores, and trying to hit an arbitrary TF-IDF number will not move your rankings. It serves strictly as an analytical lens for your own drafts to show you which concepts stand out against baseline English.

Finally, writers often look only at isolated single words. Search queries are usually two to four words long. If you only look for individual nouns, you miss how modifying adjectives and verbs frame the topic for search engines and readers.

Frequently Asked Questions

How to find keywords in a text without paid software?

You can use a free in-browser extraction tool, Google Search Console, or a spreadsheet. The simplest free method for raw drafts is pasting your copy into an online extractor that applies a stop-word filter and term weighting. For published pages, Search Console provides exact keyword search data without requiring any paid subscriptions.

How to search keywords in text on iPhone?

In Safari, open the page containing the text, tap the Share icon or the address bar, and select "Find on Page" (or type your word directly into the address bar and scroll down to the "On This Page" section). In the native Notes or Files app, open the document, tap the three dots icon in the top right corner, and select "Find in Note" to search for specific words.

How to search keywords in text messages on iPhone?

Open the Messages app on iOS, go to the main conversation list, and pull down slightly on the screen to reveal the search bar at the top. Type the keyword you want to find. iOS will display matching conversations, individual text messages containing that word, and shared links or photos matching the query.

Is TF-IDF how search engines determine page rankings?

No. TF-IDF is a traditional information retrieval formula used to score term importance relative to a corpus. Modern search engines use complex neural networks, language models, and entity understanding to rank content. TF-IDF remains useful for editors as a tool to diagnose text weighting, not as a ranking signal.