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Bert Algorithm: The Definitive Guide

BERT-Algorithm

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

1. Average of LDA & Cosine value

Introduction:

In natural language processing (NLP), latent Dirichlet allocation (LDA) does not care the order of the words in the document. Usually, LDA use the bag-of-words feature representation to represent a document.

Cosine similarity is a metric used to determine how similar the documents are irrespective of their size.

Before using Average of LDA & Cosine value

Need to check website LDA & cosine one by one which is time consuming. Then check with Landing page LDA & cosine value which is very time consuming.

This is competitor site’s cosine value

Landing page cosine value

Recommendation :

Use “VHS to Dvd” keyword most on this page for taking this keyword at first position on google search rank.

You can see competitor use most this keyword rather than landing page.

Analysis:

First take competitors and make LDA then extract all the keywords and their value.

After extracting keywords calculate cosine value of the website using keywords.

Calculate Average of LDA+Cosine value of competitors.

Compare with landing page LDA+cosine value.

Creating LDA

Extracting keywords &values

Calculating cosine value

Output:

LDA:

 

Extracting keywords:

Use in SEO:

This will help to extract all the keywords and values of each words and can do anything with that words and their values. Here we use for sum with cosine value.

USE in Daily life:

BERT will help to improve keyword rank.

Can check particular keyword how much use in your site and your competitor site.

Conclusion:

Sometime it’s important to know each words and values of those words. Using this easily can check all words & values.

2. Cosine value with Relative Percentage

Introduction:

Cosine similarity is a metric used to determine how similar the documents are irrespective of their size.

Before using this need to take all the cosine value individually from separate domain

Recommendation :

Use this keyword much more rather than competitor to come first position on google rank.

Analysis:

First take competitors and calculate cosine value. After that make average of all cosine value.

Create relative percentage of landing page. Then compare with competitors average cosine value.

Calculating cosine value and its percentage

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

Output:

Use in SEO:

This will help to calculate cosine values and compare with relative percentage very easily.

USE in Daily life:

With the help of this algorithm improve your search queries. Can easily determine which keywords needs to more use to come at first position on google rank.

Conclusion:

Using this easily can check all words cosine values. Also it’s easy to compare with landing page relative percentage.

3. Judging & Analysing The Relevance Topical Task Flow(TTF) Using NLP

Introduction:

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.

Before using The Relevance Topical Task Flow(TTF)

You need to check individual cosine value of all words one by one in the document which is much more time consuming.

With keyword’s cosine value check can see which keywords are much more use in the site.

This way all the keywords cosine value calculate. Which is time consuming.

Recommendation :

“VHS to DVD” this keyword cosine value is low. Below 0.5. This keywords cosine value need to >=0.5 . Above or equal 0.5 cosine value means keyword use most in the page.

Check all keywords cosine value at a time. Which will help to identify which keywords use most in the document. All the thing can check at single time.

Analysis:

First take competitors and use Bag of Words to extract keyword from a text document.

After extracting keywords calculate each words cosine value.

Sum all the words cosine value and compare with landing page’s relative percentage.

Creating Bag of words

Calculating all words cosine value

Sum of all words cosine value

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

Output:

Bag of words:

All words and their cosine values

Use in SEO:

This will help to easily extract all the keywords and calculate cosine values of each words and compare with relative percentage. Using this we can analyse why the rankings are dropping for some particular keyword.

USE in Daily life:

Learns contextual relations between words in a text with BERT. You can easily see all the keywords how many time use in the document.

Conclusion:

Sometime it’s important to know each words cosine values. Using this easily can check all words cosine values. Also it’s easy to compare with landing page relative percentage.

4. Stop words against query & differentiation checker

Introduction:

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.

Before using Stop words

Keyword: VHS to Dvd Service

Without Stop words

Recommendation :

Use only vhs dvd service. omit to because with out conjunctive keyword cosine value is high. That means that keyword much more use in that site. Rather than conjunctive keyword.

It nearest to 0.5 So we decide to make a algorithm which help to analyse with stopword & without stop word keywords effect.

Analysis:

First take two competitor and then calculate LDA and cosine value.

Then using stop word remover remove stopwords then calculate LDA and cosine value.

Then differentiate both with stop words and without stop words LDA and cosine values.

Creating Stop words remover

Calculating LDA

Calculating all words cosine value

Differentiate both values

Output:

Use in SEO:

This will help to easily omit stop words. Also can check with stop words and without stop words LDA+cosine values. You can compare search keywords differences with stop words and with out stop words effect of analysing time.

USE in Daily life:

BERT mainly use for improve search queries. It will help to improve your search result through keywords in daily life. You can easily see the differences between with conjunctive keywords(because,or,and) and without conjunctive keywords (because,or,and) search results.

Conclusion:

Sometime it’s important to analyse stop words how much effective for searching a query. This algorithm say your query how much different with stop words and without stop words. This is very important element BERT algorithm.

5. Topic Model Similarity

Introduction:

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.

Before using Topic Model Similarity

Topic models (LDA):

Document1 from www.tapestodigital.co.uk

Document2 from www.video2dvdtransfers.co.uk

With this algorithm can analyse which keywords are same in both document

Recommendation :

This “Year” keywords are common in the both both document. Need to omit this keyword for unique documentation.

Check which keywords are same in both documents and can change that keywords for unique identification of documents.

Analysis:

First take two competitor and then calculate LDA

Then intersect both LDA

Then calculate with landing page LDA

Again intersect with previous intersect

LDA

Intersect LDA

Output:

LDA

Two LDA after Intersect:

Use in SEO:

This will help to check which keywords are common between two or more than two sites. Using this algorithm easily can find which keywords are common each other.

USE in Daily life:

BERT mainly use for improve search result. It will help to improve your search query through keywords in daily life. You can easily see which are common keywords & can optimize or change that keywords for unique identification of document.

Conclusion:

Easily can find which keywords are similar with landing page keywords. Easily can calculate this with BERT algorithm.

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