Book Image

Deep Learning with R for Beginners

By : Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
Book Image

Deep Learning with R for Beginners

By: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

Overview of this book

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Mining sentiment from Twitter


It's time to put our knowledge of different sentiment classification models in a more realistic scenario—Twitter sentiment analysis. 

As we mentioned in the introduction, sentiment analysis is of great interest for all companies that have a presence online (which is, well, lots of companies in many countries). It is also relevant for politicians, researchers, stock traders and others. 

Note

Before using any service or API, be sure to review their terms of service and follow them! We do not encourage unlawful behavior in any way.

Connecting to the Twitter API

Luckily for us, there is a nice package in R to retrieve our Tweets: The library twitteR. First, there are a number of steps you need to follow:

  1. If you do not have one, create a Twitter account to be able to access their API.
  2. Go to https://dev.twitter.com/apps and log in with your credentials. 
  3. Once logged in, click on Create New App.
  4. Put this as callback URL http://localhost:1410.
  5. Now go to Keys and Access Tokens...