You can pull keywords from any website using a few different methods, ranging from a simple right-click on the page to dedicated SEO tools that reveal every term a site ranks for in Google. The right approach depends on whether you want to see what keywords are embedded in the page’s code, what terms appear most frequently in the visible content, or what search queries actually drive traffic to the site.
Check the HTML Source Code
Every web page has underlying HTML that often contains deliberate keyword choices made by the site owner. You can view it in any browser by right-clicking the page and selecting “View Page Source” (or pressing Ctrl+U on Windows, Cmd+Option+U on Mac). Once the source code opens, look at these specific elements:
- Title tag: Found near the top of the code inside the
<head>section, wrapped in<title>...</title>tags. This is the clickable headline that appears in search results, and it almost always contains the page’s primary target keyword. - Meta description: Also in the
<head>section, this tag provides the short summary shown beneath the title in search results. It typically includes one or two keywords the page is trying to rank for. - H1 heading: The main on-page headline, wrapped in
<h1>...</h1>tags. Most well-optimized pages have exactly one H1 that signals the core topic. - Image alt tags: Buried inside image tags as an
altattribute, these describe images for search engines and screen readers. They often reveal secondary keywords the site is targeting.
Use your browser’s Find function (Ctrl+F or Cmd+F) to search the source code for terms like “meta,” “title,” or “h1” to jump straight to these elements without scrolling through hundreds of lines of code. This method is completely free and works on any public webpage, but it only shows you keywords the site owner intentionally placed in the HTML. It won’t tell you what the page actually ranks for in search engines.
Use Browser Extensions for On-Page Analysis
Browser extensions can automate the process of scanning a page and listing every keyword it contains along with how often each one appears. Keywords Everywhere, available as a Chrome extension, offers an on-page analysis feature that breaks down the keywords found in a page’s content and calculates their density, which is the percentage of total words each keyword represents. A keyword that appears 15 times on a 1,000-word page has a density of 1.5%.
This type of analysis is useful for understanding what topics a page emphasizes. If you’re studying a competitor’s blog post, for instance, you can quickly see which phrases they repeat most and which related terms they weave into the content. Most on-page analysis extensions display results in a simple table sorted by frequency, so you get a prioritized list in seconds rather than reading through the entire page manually.
Other popular extensions for this purpose include SEOquake and Detailed SEO Extension, both free for Chrome and Firefox. They pull title tags, meta descriptions, headings, and word counts into a single dashboard without requiring you to dig through source code yourself.
Find What Keywords a Site Ranks For
The most valuable keyword data usually isn’t visible on the page at all. It’s the list of search queries that bring people to the site through Google. To get this data, you need an SEO tool that tracks search rankings across millions of keywords.
The process is straightforward with any of the major platforms: enter a competitor’s domain or a specific URL, and the tool returns a list of organic keywords the site ranks for, along with each keyword’s estimated search volume, ranking position, and traffic contribution. Four tools dominate this space:
- Semrush: An all-in-one SEO suite that tracks organic keywords, paid ad keywords, domain traffic estimates, and ranking positions over time. Enter any domain and export its full organic keyword list.
- Ahrefs: Especially strong for deep keyword analysis combined with backlink data. Its “Content Gap” feature identifies keywords your competitors rank for that you don’t, which is one of the fastest ways to find new content opportunities.
- Ubersuggest: A more affordable option that surfaces competitor keywords, keyword difficulty scores, and content ideas. Good for beginners who don’t need the full depth of Semrush or Ahrefs.
- SpyFu: Focused specifically on competitor intelligence, showing both organic and paid search strategies, including historical ad targeting and keyword changes over time.
These tools are paid services, though most offer limited free searches. Semrush and Ahrefs typically cost $99 to $129 per month for their entry-level plans. Ubersuggest offers a cheaper tier, and SpyFu sits in a similar range. If you only need a quick snapshot of a competitor’s keywords, the free searches available on each platform before hitting a paywall may be enough.
For your own website, Google Search Console provides this data for free. It shows you exactly which search queries triggered your pages in Google results, how many clicks each query generated, your average ranking position, and how often your pages appeared. This is first-party data directly from Google, making it the most accurate source for your own site’s keyword performance.
Extract Keywords With Python
If you want to extract keywords from page content programmatically, perhaps across dozens or hundreds of pages, Python offers several libraries built for exactly this. You would first scrape the text content from a URL (using a library like BeautifulSoup or requests), then pass that text through a keyword extraction algorithm.
Four widely used Python libraries handle the extraction step:
- RAKE (Rapid Automatic Keyword Extraction): Installable via
pip install multi_rake, this library identifies keyword phrases by looking at word frequency and how often words appear together. It’s fast and requires no training data. - YAKE: A statistical approach that scores keywords based on their position in the text, frequency, and relationship to other terms. Install it with
pip install git+https://github.com/LIAAD/yakeand extract the top keywords with just a few lines of code. - TextRank: Based on the same algorithm Google originally used for ranking pages, this method builds a graph of word relationships and identifies the most connected terms. Available through the
summalibrary (pip install summa). - KeyBERT: The most advanced option, using a machine learning model to understand the meaning of words rather than just counting them. Install with
pip install keybert. It produces more contextually accurate results but runs slower than the statistical methods, especially on large batches of text.
A typical workflow looks like this: scrape the page text, clean out navigation menus and footer content so you’re left with the main body, then run the text through one of these libraries with a parameter like top=10 to get the ten most relevant keywords. KeyBERT lets you specify phrase length (single words, two-word phrases, or three-word phrases) using the keyphrase_ngram_range parameter, which is helpful when you want to capture terms like “content marketing strategy” rather than just “content.”
This approach requires basic Python knowledge, but it scales well. You can loop through a list of competitor URLs and build a keyword database in minutes that would take hours to assemble manually.
Choosing the Right Method
Your goal determines which approach makes the most sense. If you’re checking whether a single page is properly optimized, viewing the source code or using a browser extension takes 30 seconds and costs nothing. If you want to know what search terms actually send traffic to a competitor’s site, you need an SEO tool like Semrush, Ahrefs, or Ubersuggest. And if you’re analyzing content at scale or building keyword lists from dozens of pages, Python scripts give you the most flexibility and speed.
For most people doing competitive research, the combination of Google Search Console for their own site and one paid SEO tool for competitor analysis covers everything. The source code and browser extension methods work best as quick checks rather than primary research strategies, since they show what’s on the page but not whether those keywords actually drive any search traffic.