Text Mining, Scraping and Sentiment Analysis with R

Text Mining, Scraping and Sentiment Analysis with R, available at $49.99, has an average rating of 4.35, with 39 lectures, 1 quizzes, based on 491 reviews, and has 4093 subscribers.

You will learn about 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 This course is ideal for individuals who are everybody interested in social media analysis or everybody interested in using R for web scraping or everybody interested in sentiment analysis or everybody interested in text analysis or everybody interested in enlarging their R toolbox It is particularly useful for everybody interested in social media analysis or everybody interested in using R for web scraping or everybody interested in sentiment analysis or everybody interested in text analysis or everybody interested in enlarging their R toolbox.

Enroll now: Text Mining, Scraping and Sentiment Analysis with R

Summary

Title: Text Mining, Scraping and Sentiment Analysis with R

Price: $49.99

Average Rating: 4.35

Number of Lectures: 39

Number of Quizzes: 1

Number of Published Lectures: 38

Number of Published Quizzes: 1

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 39

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.’

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the R-Tutorials Social Media Mining Course

Lecture 2: Package Overview: NLP

Lecture 3: Package Overview: Web Technologies and Services

Lecture 4: Course Links

Lecture 5: Course Script

Lecture 6: Worksheet – Exercises

Chapter 2: Scraping and Text Mining

Lecture 1: Twitter Developer Account

Lecture 2: Important: Connection Information

Lecture 3: Code Section: Social Media Mining

Lecture 4: Connection of R Studio with Twitter

Lecture 5: Alternative Authentication

Lecture 6: Alternative Authentication Code

Lecture 7: Scraping with TwitteR

Lecture 8: Text Mining with "tm" – Text Cleaning and Transformations

Lecture 9: Wordcloud

Lecture 10: Document Term Matrix and Frequent Terms

Lecture 11: Dendrogram and Term Groups

Lecture 12: Exercise: Text Mining

Lecture 13: Solution: Text Mining Part 1

Lecture 14: Solution: Text Mining Part 2

Lecture 15: Exercise Code: Text Mining

Lecture 16: Further R Exercises

Chapter 3: Working with Strings – gsub and the Regular Expression syntax

Lecture 1: Regular Expressions and gsub for sentiment analysis – handling of scraped data

Lecture 2: Code Section: Strings

Lecture 3: Working with Strings – Introduction

Lecture 4: Working with Strings – gsub

Lecture 5: Working with Strings – gsub advanced

Lecture 6: Regular Expression Overview

Lecture 7: Working with Strings – Library Stringr

Lecture 8: Exercise and Solution: Strings in R

Chapter 4: Sentiment Analysis

Lecture 1: Section Code: Sentiment Analysis

Lecture 2: Sentiment Analysis Basics

Lecture 3: Score Sentiment Function – J. Breen Approach

Lecture 4: Tweets for Sentiment Analysis

Lecture 5: Visualizing the Sentiments

Lecture 6: Exercise: Sentiment Analysis

Lecture 7: Solution: Sentiment Analysis

Lecture 8: Exercise Code: Sentiment Analysis

Instructors

R-Tutorials Training
Data Science Education

Rating Distribution

1 stars: 23 votes
2 stars: 25 votes
3 stars: 78 votes
4 stars: 172 votes
5 stars: 193 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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