/Text-Similarity-Score-Generator

This is a Text Similarity Score Generator. It takes in two different texts and compares how similar they are. To calculate the similarity score I am using Vector Space Model. This model creates a vector Space where each dimension represents a single word. Words are taken from all the texts that are considered. One document is a single vector space. Each dimension of a single document vector represents how often this word appears in the text.To compare two documents a cosine similarity is used. This generates a value between 0 and 1, 0 meaning no similarity and 1 meaning perfect match.

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Text-Similarity-Score-Generator

This is a Text Similarity Score Generator. It takes in two different texts and compares how similar they are. To calculate the similarity score I am using Vector Space Model. This model creates a vector Space where each dimension represents a single word. Words are taken from all the texts that are considered. One document is a single vector space. Each dimension of a single document vector represents how often this word appears in the text.To compare two documents a cosine similarity is used. This generates a value between 0 and 1, 0 meaning no similarity and 1 meaning perfect match.

#How to Use it?

1. Clone Code

git clone https://github.com/Abhishek-EE/Text-Similarity-Score-Generator.git

2. Install dependencies and Python Packages

It is recommended that you install python 3.6+ and use pip for installing the dependencies.

pip install Flask numpy pandas

3. How to run the code

Open up the terminal and reach where runserver.py file is then run

python runserver.py

you should get a message like this

  • Serving Flask app "Website" (lazy loading)
  • Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
  • Debug mode: on
  • Restarting with stat
  • Debugger is active!
  • Debugger PIN: 206-182-451
  • Running on http://localhost:5555/ (Press CTRL+C to quit)

clik on http:/localhost:5555/ to reach the server Follow the instruction there to run the Text Similarity Algorithm