tag-generator

A simple application to generate tags for the given text (document) using tf-idf.

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tf-idf

Implementation of term frequency - inverse document frequency

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What is tf-idf ?

Tf-idf stands for term frequency - inverse document frequency which is used in text mining and information retrieval system to evaluate how important a word is in a document.

The importance is directly proportional to the number of times a word appears in the document but is also weighted down by the frequency of the word in the whole corpus.

Mathematically

term-frequency (tf) of a term/word t is actually given by:

tf = (number of times the term t appears in a document ) / (total number of terms in the same document)

inverse document frequency (idf) mesaures how much rare a term is throughout the multiple documents. That is, more the rareness of a term, the greater we tend to value the rareness.

idf = natural_logarithm[ (total number of documents) / (number of documents having the term t) ]

Here, natural_logarithm is the logarithmic function with base e.

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Dependency

The only dependency I have used here (Used from June 25, 2018 and onwards) is nltk library for lemmatization purpose.

pip install nltk

Usage

First clone this repo

git clone https://github.com/NISH1001/tag-generator

Put documents (text file) inside data/documents/ path

The tagger uses the documents available inside data/documents/
So, make sure there is atleast one document inside this path.

Test the tagger

Run the tagger.py script. [I am assuming you are using python3]

python3 tagger.py

This will generate default 5 tags. Additional number can be supplied for getting more tags.

python3 tagger.py 7

If you want to test a custom text file, put that inside the data folder as data/test.

References

https://en.wikipedia.org/wiki/Tf%E2%80%93idf

http://www.tfidf.com/