word2vec

In this project, we will use the gensim library to demonstrate the use of word2vec.

TL;DR

The program learns the text corpus and finds the top 5 most probable related words to the words we input to the algorithm. This is the underlying algorithm for text prediction.

What is word2vec?

The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect words with highest cosine similarity from the text.

Software Prequisites:

Python 3.x

Instructions to Execute

1. Clone the Repo
2. Run the commands as below:

When running for the first time use the following command to to install gensim:
$ pip install -U gensim

Command to run the consumer python program:
$ python test.py fake_or_real_news.csv mymodel query_words.txt

NOTE: For changing words in query_words.txt, be careful to enter words exactly in the format below:

word1
word2
word3
wordn

Next steps for the project:

Build a flask app implementation of autocomplete