Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that involves teaching computers to understand, interpret, and generate human language. It uses techniques such as machine learning, computational linguistics, and computer science to enable machines to process and comprehend language in a way that is similar to humans. NLP has a wide range of applications, including chatbots, speech recognition, sentiment analysis, language translation, and more, and is becoming increasingly important in many industries, including healthcare, finance, and e-commerce.


Information Retrieval is the process of obtaining relevant information from a collection of text or multimedia documents. 


In SEO, we can use artificial intelligence to optimize content. Semantic AI can be used.

Semantics is the philosophical and scientific study of meaning in natural and artificial languages. It is also known as semiotics, semiology, or semasiology. The term is one of several English words derived from various derivatives.

In SEO, artificial intelligence can be used to extract data from documents. AI can use a variety of algorithms to extract information from large amounts of data.

Here are some examples of how we can optimize the content:

What is Cosine Similarity and how does it help SEO?

Cosine similarity in SEO terms measures how similar a given keyword available in the document is to the overall context of the landing page. In SEO, the more similar a keyword is to the landing page, the higher it will rank in SERPs.

Cosine similarity in SEO terms measures how similar a given keyword available in the document is to the overall context of the landing page. In SEO, the more similar a keyword is to the landing page, the higher it will rank in SERPs.

Cosine similarity ideal score

The ideal cosine similarity score for good SEO is 0.5 or higher.

Less than 0.5 is poor and should be improved.

If the result is 0, it means that the keyword is not present at all.

Work mechanism:

We begin by comparing the given keyword to the target landing page URL and computing the cosine value in% using our AI code. After that, we compare it to our competitor’s mean value and see if the data obtained is less or more than the competitor’s. 

If our value exceeds the mean value of the competitor, no action is required. Otherwise, we run our code again to see how many more occurrences we need to add to the landing page to improve the score.

What is LDA and what are its advantages in SEO?

LDA, or Latent Dirichlet Allocation, is a type of Topic modeling. 

In SEO, LDA will assist in determining the relevance score of a specific keyword as well as increasing a page’s relevancy in Google. It displays words that assist Google in determining how relevant a page is to a user’s search query. 

LDA’s ideal score

LDA’s ideal SEO score is between 0.1 and 0.3.

It’s excellent if it’s greater than 0.3. It is ideal for SEO if it is between 0.1 and 0.3.

Less than 0.1 is considered poor.

Work mechanism:

First, we scrape the entire document using the URL of our given campaign.

The relevancy signals are then computed using the LDA algorithm and calculations.

We then correlate with the competitor’s mean relevance value and use a conditional statement to determine whether the value is less or greater than the competitor’s.

If our value is higher than the competitor’s, no action is required; otherwise, we use our code structure to suggest missing terms that will help us improve our score once they are added to the landing page.

What is a Bag of Words and what are its advantages in SEO?

The bag of words model is a model for retrieving information. It finds keywords in large amounts of data. It also specifies the frequency with which the document’s keywords are used. In SEO, we use this model to create tags and correlate them with competitors’ tags to use all missing search terms when compared to competitors.

Please keep in mind that the more search terms that are targeted, the higher the SERP visibility.

Excellent SEO Value

There are no ideal values for Bag of words, and the agenda remains straightforward. We simply need to optimize our pages using the available tags based on competitive analysis. 

The more we use, the better we will contribute to improving SERP visibility. Please keep in mind that over-optimization will result in spamming.

Work mechanism:

Make a model for the landing page you want to target and collect the top ten words based on frequency. Repeat the preceding steps for each competitor to obtain the top ten high-frequency words. Merge all of the top-frequency words from the competitors and create a superset of word tags, making sure the set is unique and there is no repetition of words. Then, using the final super set word, check your landing page word and collect the words that aren’t on your landing page. Create a final list of unique words, which will be your output. In other words, we need to use this final list within our landing page to optimize the context around the page.


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Technology Authenticity

Our AI-SEO technology is Patent protected with Patent Filing Number: 202131021713

Thatware LLP is also protected under Intellectual property: CBR IP 6979