Peer-graded Assignment: Final Project Submission

According with the instructions, I've used the information of Twitter, Blogs and News.

• Taking into account that the files are very big (>200 MB), I've sampled the 10%.

The nexts slides, explain the algortihm used and how the app was constructed.

When I started the Capstone Project, I tried the same approach the majority in Coursera had done it, with n-grams; however, I wanted to try another thing, and looking for something different, I find the Word2Vec algorithm.

Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.

Word2vec was created by a team of researchers led by Tomas Mikolov at Google. The algorithm has been subsequently analysed and explained by other researchers.[2][3]. You can read information of the Word2Vec algortihm in google or here.

I used Word Vectors to implement the Word2Vec algorithm. Thanks to Benjamin Schmidt.Word Vector is An R package for building and exploring word embedding models. See Github

Description

This package does three major things to make it easier to work with word2vec and other vectorspace models of language.

• You can prepare de sample data.
• Obtain the model.
• And predict