Topic Model Using Bert
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.
Principles of Mechanism
Here we take competitors keywords and landing page keywords then find which keywords are common between landing page and competitors keywords.
I have taken competitors to collect words
CESARCASINO
SLOTSUP
Similar Words Words Between Competitors and Landing Page
Applied Semantic
Using Code
This is called package which will help to scrap the website & run the program.
- 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.
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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/
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
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