In the first chapter, you will learn how to apply qdap's sentiment function called <a href=https://www.rdocumentation.org/packages/qdap/topics/polarity> polarity() </a>. 

Let's talk about our feelings

Jump right in! Visualize polarity

TM refresher (I)

TM refresher (II)

How many words do YOU know? Zipf's law & subjectivity lexicon

What is a subjectivity lexicon?

Where can you observe Zipf's law?

Polarity on actual text

Explore qdap's polarity & built-in lexicon

Happy songs!

LOL, this song is wicked good

Stressed Out!

Fast & Dirty: Polarity scoring

In the second chapter you will explore 3 subjectivity lexicons from tidytext.  Then you will do an inner join to score some text.

Plutchik's wheel of emotion, polarity vs. sentiment

One theory of emotion

DTM vs. tidytext matrix

Getting Sentiment Lexicons

Bing lexicon with an inner join explanation

Bing tidy polarity: Simple example

Bing tidy polarity: Count & pivot the white whale

Bing tidy polarity: Call me Ishmael (with ggplot2)!

AFINN & NRC methodologies in more detail

AFINN: I'm your Huckleberry

The wonderful wizard of NRC

Sentiment Analysis the tidytext Way

Make compelling visuals with your sentiment output.

Parlor trick or worthwhile?

Real insight?

Unhappy ending? Chronological polarity

Word impact, frequency analysis

Introspection using sentiment analysis

Divide & conquer: Using polarity for a comparison cloud

Emotional introspection

Compare & contrast stacked bar chart

Interpreting visualizations

Kernel density plot

Box plot

Radar chart

Treemaps for groups of documents

Visualizing Sentiment

Is your property a good rental?  What do people look for in a good rental?

Refresher on the text mining workflow

Step 1: What do you want to know?

Step 2: Identify Text Sources

Quickly examine the basic polarity

Step 3: Organize (& clean) the text

Create Polarity Based Corpora

Create a Tidy Text Tibble!

Compare Tidy Sentiment to Qdap Polarity

Step 4: Feature Extraction & Step 5: Time for analysis... almost there!

Assessing author effort

Comparison Cloud

Scaled Comparison Cloud

Step 6: Reach a conclusion

Confirm an expected conclusion

Choose a less expected insight

Your turn!

Case study: Airbnb reviews

Line by line polarity for 4 books

4 books as a tidy data frame

4 books as DocumentTermMatrices

Polarity scores of Boston Airbnb reviews

Housing rental reviews from Airbnb in Boston

Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service, and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You’ll start with an introduction to polarity scoring using qdap’s sentiment function, and will build your understanding of Zipf’s law and subjectivity lexicons along the way.
<br><br>
Sentiment, and the language used to express it, is complicated and nuanced. It’s based on linguistics, sociology, and psychology, as well as culture and slang. The second chapter in this course helps you navigate those difficulties using Plutchik’s wheel of emotion, and organizes your work using Tidytext from the Tidyverse.
<br><br>
Turning your sentiment analysis into clear data visualizations will help you create a clearer narrative and share your insights with the rest of the business. The third chapter of this course shows you how to visualize your sentiment analysis, and takes you beyond word clouds to create simple and impactful graphics that tell the full story of your data.
<br><br>
You’ll finish off the course by putting all of your knowledge to the test with a case study. Using Airbnb reviews, you’ll explore what people really look for in a good rental.

<h2>Expand Your Text Mining Skill Set </h2>
Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service, and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You’ll start with an introduction to polarity scoring using qdap’s sentiment function, and will build your understanding of Zipf’s law and subjectivity lexicons along the way. 
<br><br>
<h2>Use Tidytext to Perform Sentiment Analysis </h2>
Sentiment, and the language used to express it, is complicated and nuanced. It’s based on linguistics, sociology, and psychology, as well as culture and slang. The second chapter in this course helps you navigate those difficulties using Plutchik’s wheel of emotion, and organizes your work using Tidytext from the Tidyverse. 
<br><br>
<h2>Bolster Your Insights with Sentiment Analysis Visualizations </h2>
Turning your sentiment analysis into clear data visualizations will help you create a clearer narrative and share your insights with the rest of the business. The third chapter of this course shows you how to visualize your sentiment analysis, and takes you beyond word clouds to create simple and impactful graphics that tell the full story of your data. 
<br><br>
You’ll finish off the course by putting all of your knowledge to the test with a case study. Using Airbnb reviews, you’ll explore what people really look for in a good rental. 

Text Mining with Bag-of-Words in R

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Sentiment Analysis in R

Exploration de texte en R

Data Dude

Subjectivity lexicon

Where to get subjectivity lexicons?

library(lexicon)

No way! Too few words.

Zipf's Law in action

Principle of Least Effort

Up next...

Let's practice!

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Fast & Dirty: Polarity scoring - How many words do YOU know? Zipf's law & subjectivity lexicon

How many words do YOU know? Zipf's law & subjectivity lexicon

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