R Tutorial: Sentiment Analysis | Let's talk about our feelings

Welcome to Sentiment Analysis in R. In this course we build upon your previous Bag of Words tool set and introduce basic methods for sentiment analysis on books, lyrics and apply it to a real use case. First, let's start by defining sentiment analysis. Sentiment analysis, as I think about it, is defined as the process of extracting an author's emotional intent from from text. This can take a lot of forms, from simple positive and negative feelings to specific emotions like surprised or angry. Sentiment analysis is used every day in various applications. For example, academics study ethnographic interviews. Marketers apply sentiment analysis to understand how an ad campaign is affecting a customer's brand perception over time and and some even trade stocks based on company announcements and their sentiment. The emotion that an author tries to convey in a piece of writing is often important. Metadata sentiment analysis is important because it unlocks the emotions from messy text data into a structured form that is easy to analyze and quantify. In contrast, Bag of Words methods suggest simply organize text and perform matrix analyses without regard to the author's emotion. In this course, we still borrow Bag of Words tools like the document term matrix or vector based analysis, but also add the tidy text data format called a tibble. In chapter one we will cover basic polarity, that is to say the degree of positive or negative expression within text. It's quick and dirty, but lets you jump right in. Largely. The tidytext method uses simple database join techniques to match text to known expressive words like awful or wonderful. You will learn the basics of the inner join and other tidy principles in this course. Plus, datacamp has several courses on tidying data since so many analyses require a visual readout. Chapter three is devoted to visualizing sentiment outputs. For example, tracking sentiment over time would use a line chart, while comparing emotions could utilize a radar chart. For added learning. We introduced some of the JavaScript visuals from htmlwidgets.org lastly, we wrap it all up in a real case study. You will perform a sentiment analysis on real property rental reviews in the Boston area. In this use case you are trying to understand what makes a good rental experience versus a poor one in case you want to rent out your own place. At the end of the course, you will understand the basics of sentiment analysis and be able to apply it to text using R. Let's get to it.