Text Mining, Scraping and Sentiment Analysis with R

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.'