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R Deep Learning Projects

You're reading from R Deep Learning Projects Master the techniques to design and develop neural network models in R

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788478403

Length 258 pages

Edition 1st Edition

Languages

R

Tools

Keras

Concepts

Deep Learning

Authors (2):

Yuxi (Hayden) Liu

Pablo Maldonado

View More author details

Table of Contents (7) Chapters

Preface

1. Handwritten Digit Recognition Using Convolutional Neural Networks

2. Traffic Sign Recognition for Intelligent Vehicles FREE CHAPTER

3. Fraud Detection with Autoencoders

4. Text Generation Using Recurrent Neural Networks

5. Sentiment Analysis with Word Embeddings

6. Other Books You May Enjoy

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Sentiment Analysis with Word Embeddings

In this chapter, we turn to the problem of sentiment analysis. Sentiment analysis is an umbrella term for a number of techniques to figure out how a speaker feels about a certain topic or piece of content.

A vanilla case study of sentiment analysis is polarity. Given a document or text string (for instance, a Tweet, a review, or a comment on a social network), the aim is to determine whether the author feels good, bad, or neutral about the item or topic in question.

At first look, this problem might seem trivial: A lookup table with positive and negative words, and simply counting the word frequencies should do, right? Not so fast. Here are a few examples of why this is tricky:

Their decadent desserts made me hate myself
You should try this place if you love cold food
Disliking cake is not really my thing

What can we see in...

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