Meta tags help improve your web pages and help search engines understand the What are the most topic of your content. Often misunderstood, these little korea telegram data tags have a bad reputation.
For a long time, it was popular to try to trick search engine algorithms by repeating keywords in metadata ( keyword stuffing ). This caused Google to change its algorithms and evaluate meta tags differently.
In this guide, we’ll show you how to optimize meta tags to improve your SEO. We’ll look at how meta tags have evolved and identify which ones are useful for SEO and which ones are best avoided.
What is a Meta tag?
Meta tags are small pieces of HTML code that help describe the content of your webpage to Google and other search engines. The word “Meta” is short for metadata. This is additional data about your webpage, such as the page title, description, and content type. Search engines use data from meta tags in their search results pages (SERPs).
Check your meta tags and identify issues that affect your website’s indexability.
Do Meta tags have an impact on SEO?
Meta tags are still necessary and very useful for SEO today. They have been a staple of SEO strategies since the beginning of search engine optimization. Although their use and value have evolved over the years, they should not be overlooked.
Meta and HTML tags
Metadata is found in the header of all your web months later, jim’s revenue pages and articles. You can easily add your meta tag in HTML using a WordPress plugin or a CMS content editor. Simply insert it into your HTML and it will be visible to crawlers.
To rank well, it’s important to use the right tags. These tags can have a significant impact on your rankings and, when used correctly, improve the user experience.
I consider the two most useful meta tags to be:
- The title tag
- The Meta description
Meta tags and Google
Each search engine evaluates tags differently. However, I recommend following the guidelines below. Pay attention to the differences and adapt them to suit your website’s needs.
RAKE
Rapid Automatic Keyword Extraction (RAKE) is a well-known keyword extraction method that uses a list of stopwords and phrases as “delimiters” to detect the most relevant words or phrases in a text.
Take the following text as an example :
Following the invasion of Stargate by aliens, Colonel Jack O’Neill is called to the rescue. Stargate SG-1 is then formed and sent to explore all these new worlds.
The first thing the method does is divide the text into a list of words and remove stop words from this list. This results in a list containing so-called content words .
Let’s say our list of keywords and phrases looks like this:Our list of 8 content words will look like this
If we divide the degree divided by the frequency of each of the words in our example, we would get:
If two keywords or key phrases appear together in the same order more than twice, a new key phrase is created , regardless of the number of stop words it contains. The score for this key phrase is considered cn leads in the same way as for single key phrases.
A keyword or keyphrase is selected when its score is among the top T scores, where T is the number of keywords you want to extract .
Example with RAKE NLTK
RAKE NLTK is a Python-specific implementation of the RAKE (Rapid Automatic Keyword Extraction) algorithm that uses NLTK under the hood. This makes it easy to use for other text analysis tasks.