Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

Arrow left icon
Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
Arrow right icon
View More author details
Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Performing sentiment prediction using LSTM network


In this section, we will use LSTM networks to perform sentiment analysis. Along with the word itself, the LSTM network also accounts for the sequence using recurrent connections, which makes it more accurate than a traditional feed-forward neural network.

Here, we shall use the movie reviews dataset text2vec from the cran package. This dataset consists of 5,000 IMDb movie reviews, where each review is tagged with a binary sentiment flag (positive or negative).

How to do it...

Here is how you can proceed with sentiment prediction using LSTM:

  1. Load the required packages and movie reviews dataset:
load_packages=c("text2vec","tidytext","tensorflow") 
lapply(load_packages, require, character.only = TRUE) 
data("movie_review") 
  1. Extract the movie reviews and labels as a dataframe and matrix respectively. In movie reviews, add an additional attribute "Sno" denoting the review number. In the labels matrix, add an additional attribute related to negative...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image