Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

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...