AI Implementations in SEO

1. LDA Cosine

LDA

Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. In terms of topic modeling, the composites are documents and the parts are words and/or phrases (phrases n-words in length are referred to as n-grams).

Topic models provide a simple way to analyze large volumes of unlabeled text. Topic modeling is a method for unsupervised classification of documents. Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words. This allows documents to “overlap” each other in terms of content, rather than being separated into discrete groups, in a way that mirrors typical use of natural language.

Although LDA has many applications in many various fields, we used LDA to find out the keyword’s belonging probability in particular topic from a particular document. Each and every keyword has a belonging probability in particular topic which also indicates their relevance to a particular document.

Cosine Similarity

Cosine Similarity is a measure of similarity between two non-zero vectors that estimates the cosine angle between them. If the cosine angle orientations between two vectors are the same then they have a cosine similarity of 1 and also with different orientation the cosine similarity will be 0 or in between 0-1. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in [0, 1].

Keyword : super spade URL: https://mybettingdeals.com/glossary/s/super-spade-games/

Principles of Mechanism 

URL: https://mybettingdeals.com/glossary/s/super-spade-games/

Keyword: super spade

Cosine value:

Cosine value will tell us how much keyword available in the document. If cosine value is  0 that means keyword is not present in the document. If cosine value is 0.5 or greater than 0.5 that means keyword presence in the document is very good & as well as keyword rank is definitely good.

Applied Semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website
2. Calculate  cosine value
3. Calculate LDA

output

Before LDA cosine keyword rank

keyword rank is in 6th page of the google rank.

After LDA cosine keyword rank

We can see that rank is increases and came in the first page of the google rank.

2. Cosine with Relative Percentage Using BERT

BERT stands for Bidirectional Encoder Representations from Transformers

It is Google’s neural network-based technique for natural language processing (NLP) pre-training. BERT helps to better understand what you’re actually looking for when you enter a search query. BERT can help computers understand language a bit more like humans do. BERT helps better understand the nuances and context of words in searches and better match those queries with more relevant results. It is also used for featured snippets

Cosine Similarity

Cosine Similarity is a measure of similarity between two non-zero vectors that estimates the cosine angle between them. If the cosine angle orientations between two vectors are the same then they have a cosine similarity of 1 and also with different orientation the cosine similarity will be 0 or in between 0-1. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in [0, 1].

Keyword : best betting deals

URL:https://mybettingdeals.com/best-betting-deals/

Principles of Mechanism 

URL: https://mybettingdeals.com/best-betting-deals/

Keyword: best betting deals

Cosine value:

Cosine value will tell us how much keyword available in the document. If cosine value is  0 that means keyword is not present in the document. If cosine value is 0.5 or greater than 0.5 that means keyword presence in the document is very good.

Relative  Percentage

Formula is:

(Final value-initial value)/initial value *100

Always initial value start with= 0.5

Cosine value of landing page  taken as final value

observation

Proposed Semantic

Competitors Compare with landing page

We can see that Landing page  cosine value is smaller than average of competitors cosine value

Applied Semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website
2. Calculate  cosine value
3. Calculate Relative percentage

output

Before using BERT- keyword rank

keyword rank is in 2nd page of the Google rank.

After  using BERT- keyword rank

We can see that rank is increases and came in the first page of the google rank.

3. Bag of Words

Bag of Words, in short BOW is use to extract featured keyword from a text document. These features can be used for training machine learning algorithms. Basically speaking Bag of words is an algorithm that counts how many times a word appears in a document. Word counts allow us to compare documents and their similarities and it is used in many various fields.

Keyword : bovada URL: https://mybettingdeals.com/sports-betting/bovada-sports-betting/

As we can see that  landing page use bovadakeyword 49 times

Principles of Mechanism 

Bag of words tell us how many important words available in the document as well as tell how many time they appear.  we can see here “bovada keyword available in the document 49 times. Now we will compare with competitor and check how many time they use bovada keyword in the document. Then we will try to add the keyword in the content of landing page and try to increase the rank.

observation

I have taken competitors to compare bag of words  value of particular keyword  

https://www.bovada.lv/

As we can see that competitors use bovada keyword much more rather than landing page. For this reason frequency of the word on the competitor sites are high.

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website
2. Fetching words

output

Before Bag of words keyword rank

keyword rank is  “NA”  of the google rank.

After Bag of words keyword rank

We can see that rank is increases and came in 31st position of the google rank.

4. Topic Model Using BERT

BERT stands for Bidirectional Encoder Representations from Transformers

It is Google’s neural network-based technique for natural language processing (NLP) pre-training. BERT helps to better understand what you’re actually looking for when you enter a search query. BERT can help computers understand language a bit more like humans do. BERT helps better understand the nuances and context of words in searches and better match those queries with more relevant results. It is also used for featured snippets.

In machine learning and natural language processing a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents. Topic modelling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. LDA is also a topic model.

URL:https://mybettingdeals.com/online-casinos/

Principles of Mechanism 

Here we take competitors keywords and landing page keywords then find which keywords are common between landing page and competitors keywords.

observation

I have taken competitors to collect words

https://www.caesarscasino.com/

https://www.slotsup.com/online-casinos

Similar words between competitors and landing page

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website

output

Before using BERT- keyword rank

keyword rank is in “NA”  of the Google rank.

After  using BERT- keyword rank

We can see that rank is increases and came in the first page of the google rank.

5. Hierarchical Clustering

BERT stands for Bidirectional Encoder Representations from Transformers

It is Google’s neural network-based technique for natural language processing (NLP) pre-training. BERT helps to better understand what you’re actually looking for when you enter a search query. BERT can help computers understand language a bit more like humans do. BERT helps better understand the nuances and context of words in searches and better match those queries with more relevant results. It is also used for featured snippets

In machine learning and natural language processing a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents. Topic modelling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. LDA is also a topic model.

URL:https://mybettingdeals.com/online-casinos/

observation

I have taken competitors to compare bag of words  value of particular keyword  

https://www.betpoint.it/

sgdigital.com/game360-extends-reach-with-betpoint-in-italy

rcdmallorca.es/en/content/news/news/el-rcd-mallorca-goes-to-menorca-with-betpoint

As we can see that competitors grouping is small rather than landing page grouping. So landing page have seven groups of cluster available.

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website

output

Before Hierarchical clustering keyword rank

keyword rank is  in 2nd page  of the google rank.

After Hierarchical clustering keyword rank

We can see that rank is increases and came in 1st page of the google rank.

6. Cosine Similarity

Cosine Similarity

Cosine Similarity is a measure of similarity between two non-zero vectors that estimates the cosine angle between them. If the cosine angle orientations between two vectors are the same then they have a cosine similarity of 1 and also with different orientation the cosine similarity will be 0 or in between 0-1. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in [0, 1].

Keyword : android casino bonus

URL: https://mybettingdeals.com/android-casinos/

principle of mechanism

URL: https://mybettingdeals.com/android-casinos/

Keyword: android casino bonus

Cosine value:

Cosine value will tell us how much keyword available in the document. If cosine value is  0 that means keyword is not present in the document. If cosine value is 0.5 or greater than 0.5 that means keyword presence in the document is very good & as well as keyword rank is definitely good.

Here the result came above 0.5 so keyword of this landing page cosine value is very good. That means this keyword use in my landing page very frequently.

observation

I have taken competitors to compare cosine value  

https://www.nodepositkings.com/no-deposit-casino-mobile/

http://www.nodepositrewards.com/mobile/

https://www.casinobonuscheck.com/android-bonus/

As we can see that competitors grouping is small rather than landing page grouping. So landing page have seven groups of cluster available.

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website

2. Calculate  cosine value

output

Before cosine keyword rank

keyword rank is in 4th  page of the google rank.

After  cosine keyword rank

We can see that rank is increases and came in the first page of the google rank.

7. Stop Words Checking Using BERT

BERT stands for Bidirectional Encoder Representations from Transformers.

It is Google’s neural network-based technique for natural language processing (NLP) pre-training. BERT helps to better understand what you’re actually looking for when you enter a search query. BERT can help computers understand language a bit more like humans do. BERT helps better understand the nuances and context of words in searches and better match those queries with more relevant results. It is also used for featured snippets.

Stop word is a commonly used word that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.

Keyword : online casino

URL:https://mybettingdeals.com/online-casinos/

Mechanism

URL: https://mybettingdeals.com/online-casinos/

Keyword : online casino

Cosine value:

Cosine value will tell us how much keyword available in the document. If cosine value is  0 that means keyword is not present in the document. If cosine value is 0.5 or greater than 0.5 that means keyword presence in the document is very good.

Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. In terms of topic modeling, the composites are documents and the parts are words and/or phrases (phrases n-words in length are referred to as n-grams).

Topic models provide a simple way to analyze large volumes of unlabeled text. Topic modeling is a method for unsupervised classification of documents. Latent Dirichlet  allocation (LDA) is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words. This allows documents to “overlap” each other in terms of content, rather than being separated into discrete groups, in a way that mirrors typical use of natural language.

Although LDA has many applications in many various fields, we used LDA to find out the keyword’s belonging probability in particular topic from a particular document. Each and every keyword has a belonging probability in particular topic which also indicates their relevance to a particular document.

observation

I have taken competitors to Differentiate.

First I search with stop words two competitors and make lda cosine of two competitors then search without stop words two competitors and make LDA cosine. Then differentiate those result and compare with landing page lda cosine.

https://www.caesarscasino.com/

https://www.casinobonuscheck.com/android-bonus/

As we can see that competitors grouping is small rather than landing page grouping. So landing page have seven groups of cluster available.

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website

output

Before using BERT- keyword rank

keyword rank is in “NA”  of the Google rank.

After  using BERT- keyword rank

We can see that rank is increases and came in the first page of the google rank.

8. LDA Cosine using BERT

BERT stands for Bidirectional Encoder Representations from Transformers.

It is Google’s neural network-based technique for natural language processing (NLP) pre-training. BERT helps to better understand what you’re actually looking for when you enter a search query. BERT can help computers understand language a bit more like humans do. BERT helps better understand the nuances and context of words in searches and better match those queries with more relevant results. It is also used for featured snippets

LDA

Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. In terms of topic modeling, the composites are documents and the parts are words and/or phrases (phrases n-words in length are referred to as n-grams).

Topic models provide a simple way to analyze large volumes of unlabeled text. Topic modeling is a method for unsupervised classification of documents. Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. It treats each document as a mixture of topics, and each topic as a mixture of words. This allows documents to “overlap” each other in terms of content, rather than being separated into discrete groups, in a way that mirrors typical use of natural language.

Although LDA has many applications in many various fields, we used LDA to find out the keyword’s belonging probability in particular topic from a particular document. Each and every keyword has a belonging probability in particular topic which also indicates their relevance to a particular document.

Cosine Similarity

Cosine Similarity is a measure of similarity between two non-zero vectors that estimates the cosine angle between them. If the cosine angle orientations between two vectors are the same then they have a cosine similarity of 1 and also with different orientation the cosine similarity will be 0 or in between 0-1. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in [0, 1].

Keyword : super spade

URL: https://mybettingdeals.com/glossary/s/super-spade-games/

No keywords available for LDA value.

observation

Proposed Semantic

Competitors Compare with landing page, we can see that Landing page  LDA cosine value is smaller than average of competitors LDA cosine value

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website

output

Before using BERT keyword rank

keyword rank is in 6th page of the Google rank.

After using BERT keyword rank

We can see that rank is increases and came in the first page of the Google rank.

9. NLP using BERT

BERT stands for Bidirectional Encoder Representations from Transformers.

It is Google’s neural network-based technique for natural language processing (NLP) pre-training. BERT helps to better understand what you’re actually looking for when you enter a search query. BERT can help computers understand language a bit more like humans do. BERT helps better understand the nuances and context of words in searches and better match those queries with more relevant results. It is also used for featured snippets

Natural language processing is also known as NLP. It deals with the interaction between computers and humans. In SEO, NLP is used to classify your content. This will help search engine index.

URL:  https://mybettingdeals.com/online-casinos/

Keyword :  best betting deals

No keywords available for LDA value.

Principles of Mechanism 

URL: https://mybettingdeals.com/online-casinos/

We use NLP using BERT to retrieve all the words from the web site and calculate cosine  value of all words and make  total of all cosine value and then compare with relative percentage of a particular keyword.

Relative  Percentage

Formula is:

(Final value-initial value)/initial value *100

Always initial value start with= 0.5

Cosine value of landing page  taken as final value

Observation

URL: https://mybettingdeals.com/online-casinos/

Main focus of this Algorithm is to check all the words frequencies with cosine value in the web page . Then check which words cosine value is low. Identify those words then try to increases  the cosine value of those words in the web page.

Here can see that marked words cosine values are very low. Those words need to increase cosine value.

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website

output

Before using BERT keyword rank

keyword rank is in 3rd  page of the Google rank.

After using BERT keyword rank

We can see that rank is increases and came in the first page of the Google rank.

10. TF-IDF Report

TF-IDF stands for Term Frequency-Inverse Document frequency. The tf-idf weight in often used to indicate a keyword’s relevance of a particular

URL: https://mybettingdeals.com/glossary/b/betpoint-group-limited/

Observation

I have taken competitor to compare TF_idf term frequencies of words

https://www.betpointgroup.com/

sgdigital.com/game360-extends-reach-with-betpoint-in-italy

casino.guru/betpoint-casino-it-review

As we can see that landing page keywords compare with competitors keywords for make tf_idf. As we can see that sports book keyword available on first two site compare with competitors. But compare with third competitor we can see that landing page not showing sports book keyword. It happen Because of landing page compare term frequency with competitors.

applied semantic

This is called package which will help to scrap the website & run the program.

1. Scrap the website
2. Fetching words

output

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