Text Mining, Scraping and Sentiment Analysis with R
4.2 (382 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,313 students enrolled

Text Mining, Scraping and Sentiment Analysis with R

Learn how to use Twitter social media data for your R text mining work.
4.2 (382 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,313 students enrolled
Last updated 11/2017
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This course includes
  • 2.5 hours on-demand video
  • 9 articles
  • 6 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • use R for social media mining
  • get data from Twitter to do text analysis

  • perform web scraping tasks using the twitteR package

  • know which packages to use for web scraping
  • get R and Twitter connected
  • know how to perform a sentiment analysis in R
  • plot text data visualizations
Requirements
  • intermediate R knowledge is required (R Level 1 course)
  • R program ready on your computer
  • basic understanding of social media and web technologies
Description

Are you an advanced R user, looking to expand your R toolbox?

Are you interested in social media sentiment analysis?

Do you want to learn how you can get and use Twitter data for your R analysis?

Do you want to learn how you can systematically find related words (keywords) to a search term using Twitter and R?

Are you interested in creating visualizations like wordclouds out of text data?

Do you want to learn which R packages you can use for web scraping and text analysis purposes?

If YES came to your mind to some of those points – this course might be tailored towards your needs!

This course will teach you anything you need to know about how to handle social media data in R. We will use Twitter data as our example dataset.

During this course we will take a walk through the whole text analysis process of Twitter data.

At first you will learn which packages are available for social media analysis.

You will learn how to scrape social media (Twitter) data and get it into your R session.

After that we will filter, clean and structure our text corpus.

The next step is the visualization of the text data via wordclouds and dendrograms.

And in the last section we will do a whole sentiment analysis by using a common word lexicon.

All of those steps are accompanied by exercise sessions so that you can check if you can put the information to work.

According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own.

Disclaimer required by Twitter: 'TWITTER, TWEET, RETWEET and the Twitter logo are trademarks of Twitter, Inc or its affiliates.'

Who this course is for:
  • everybody interested in social media analysis
  • everybody interested in using R for web scraping
  • everybody interested in sentiment analysis
  • everybody interested in text analysis
  • everybody interested in enlarging their R toolbox
Course content
Expand all 38 lectures 03:08:31
+ Introduction
6 lectures 15:52

Those are the packages you can use in R for natural language processing tasks.

Preview 08:11

Packages for web related tasks like scraping.

Preview 04:47
Course Links
00:27
Course Script
17 pages

This worksheet contains the exercises and solutions. Note that there is also the yahoo finance screenshot for exercise 2 available as download.

Worksheet - Exercises
10 pages
+ Scraping and Text Mining
16 lectures 01:05:34

You will learn how to get a Twitter developer account and which info you need to get from that account.

Twitter Developer Account
04:20
Important: Connection Information
00:35
Code Section: Social Media Mining
02:53

In this video I will show you the needed R code to get the scraping process going.

Connection of R Studio with Twitter
05:58
Alternative Authentication
03:45

In case the standard way to connect to Twitter does not work, here is an alternative code.

Alternative Authentication Code
00:31

We will use the searchTwitter function to scrape data from Twitter.

Preview 08:50

In this video I will show you how to clean a text corpus (punctuation, space, stopwords) with the "tm" package. We will also do some transformations for easier text mining.

Text Mining with "tm" - Text Cleaning and Transformations
04:39

Let`s plot a wordcloud with our data!

Wordcloud
05:58

In this video I will show you how to get a document term matrix from your corpus. I will also show you how you can extract the most frequent terms from your data.

Document Term Matrix and Frequent Terms
05:32

We will create a dendrogram and we will also identify the term groups in the data.

Dendrogram and Term Groups
03:46
Exercise - Quiz
7 questions
Exercise: Text Mining
02:23
Solution: Text Mining Part 1
06:07
Solution: Text Mining Part 2
06:34
Exercise Code: Text Mining
01:02
Further R Exercises
02:39
+ Working with Strings - gsub and the Regular Expression syntax
8 lectures 30:42
Regular Expressions and gsub for sentiment analysis - handling of scraped data
00:32
Code Section: Strings
02:24
Working with Strings - Introduction
05:45
Working with Strings - gsub
07:42
Working with Strings - gsub advanced
05:23
Regular Expression Overview
2 pages
Working with Strings - Library Stringr
05:10
Exercise and Solution: Strings in R
03:46
+ Sentiment Analysis
8 lectures 51:25
Section Code: Sentiment Analysis
04:29

You will learn the theory behind a sentiment analysis and I will also walk you through a simple example.

Sentiment Analysis Basics
12:01

We will take a closer look at our main function for sentiment analysis.

Score Sentiment Function - J. Breen Approach
07:33

We will get Tweets to compare the sentiment on 4 different countries.

Tweets for Sentiment Analysis
04:13

We will score our Tweets and visualize the data.

Visualizing the Sentiments
05:46
Exercise: Sentiment Analysis
02:06
Exercise Code: Sentiment Analysis
03:08