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R Deep Learning Projects

You're reading from R Deep Learning Projects

Product type Book

Published in Feb 2018

Publisher Packt

ISBN-13 9781788478403

Pages 258 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (11) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Handwritten Digit Recognition Using Convolutional Neural Networks

2. Traffic Sign Recognition for Intelligent Vehicles

3. Fraud Detection with Autoencoders

4. Text Generation Using Recurrent Neural Networks

5. Sentiment Analysis with Word Embeddings

1. Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

Chapter 5. 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 these examples?

Negative terms used in a possibly positive sense
Positive terms used sarcastically
Two negative terms that...

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