Comparison against TF-IDF Vectorizer (using sklearn)

[BALaka-18]

Description

TF-IDF is one of the most famous algorithms when it comes to keyword extraction from text. Your task is to create a function that will extract keywords from text using the TF-IDF algorithm and compare the results against this library. How similar / different are the results ?

For reference :

For your reference, you may read these :

1. Keyword extraction
2. TF-IDF Vectorizer - Sklearn docs

Folder Structure, Function details

Create a folder tfidf_vectorizer in the root directory. The folder must contain a .py file that will contain the function for extracting the keywords from text using sklearn's TfidfVectorizer.

Structure : tfidf_vectorizer/extract_keywords_tfidf_sklearn.py

Acceptance Criteria

• Code must be properly formatted.
• Code must be accompanied by appropriate comments.
• File structure must be strictly maintained.
• Test cases must be present at the end of the code.
• Variables and functions must be properly named
• IMPORTANT : Make sure requirements.txt file is updated if you are including any new library.
• All instructions provided in the Description must be strictly followed.

Definition of Done

• All of the required items are completed.
• Approval by 1 mentor.

Time Estimation

1.5 hours

[rexdivakar]

Assign it to me @BALaka-18

[BALaka-18]

@rexdivakar assigned.