Text Mining and Sentiment Analysis with Tableau and R

Extract valuable info out of Twitter for marketing, finance, academic or professional research and much more.

This course harnesses the upside of R and Tableau to do sentiment analysis on Twitter data. With sentiment analysis you find out if the crowd has a rather positive or negative opinion towards a given search term. This search term can be a product (like in the course) but it can also be a person, region, company or basically anything as long as it is mentioned regularly on Twitter.

While R can directly fetch the text data from Twitter, clean and analyze it, Tableau is great at visualizing the data. And that is the power of the method outlined in this course. You get the best of both worlds, a dream team.

Content overview and course structure:

The R Side

Getting a Twitter developers account

Connection of Twitter and R

Getting the right packages for our approach

Harvesting Tweets and loading them into R

Refining the harvesting approach by language, time, user or geolocation

Handling Twitter meta data like: favorites, retweets, timelines, users, etc

Text cleaning

Sentiment scoring via a simple lexicon approach (in English)

Data export (csv) for further Tableau work

Tableau Side:

Data preparation for visualizations

Quick data exploration

Dashboards

Visualizing - 

• Popularity of different products
• Popularity between different locations on a map
• Changes in popularity over time

You only need basic R skills to follow along. There is a free version of Tableau called Tableau public desktop, or even better: as a full time college student you can get a free but full version of Tableau desktop professional.

The course comes with the R code to copy into your R session.

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