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Janesh Devkota has completed

Sentiment Analysis in R: The Tidy Way

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4 hours
4,350 XP
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Course Description

Text datasets are diverse and ubiquitous, and sentiment analysis provides an approach to understand the attitudes and opinions expressed in these texts. In this course, you will develop your text mining skills using tidy data principles. You will apply these skills by performing sentiment analysis in several case studies, on text data from Twitter to TV news to Shakespeare. These case studies will allow you to practice important data handling skills, learn about the ways sentiment analysis can be applied, and extract relevant insights from real-world data.
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  1. 1

    Tweets across the United States

    Free

    In this chapter you will implement sentiment analysis using tidy data principles using geocoded Twitter data.

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    Sentiment analysis and tidy tools
    50 xp
    Sentiment lexicons
    100 xp
    Words in lexicons
    50 xp
    Sentiment analysis via inner join
    50 xp
    Implement an inner join
    100 xp
    Understanding an inner join
    50 xp
    Using dplyr verbs to analyze sentiment analysis results
    50 xp
    What are the most common sadness words?
    100 xp
    What are the most common joy words?
    100 xp
    Looking at differences by state
    50 xp
    Do people in different states use different words?
    100 xp
    Which states have the most positive Twitter users?
    100 xp
  2. 2

    Shakespeare gets Sentimental

    Your next real-world text exploration uses tragedies and comedies by Shakespeare to show how sentiment analysis can lead to insight into differences in word use. You will learn how to transform raw text into a tidy format for further analysis.

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    Tidying Shakespearean plays
    50 xp
    To be, or not to be
    100 xp
    Unnesting from text to word
    100 xp
    Sentiment analysis of Shakespeare
    100 xp
    Using count and mutate
    50 xp
    Tragedy or comedy?
    100 xp
    Most common positive and negative words
    100 xp
    Which word was misidentified?
    50 xp
    Sentiment contributions by individual words
    50 xp
    Word contributions by play
    100 xp
    Calculating a contribution score
    100 xp
    Alas, poor Yorick!
    100 xp
    Which words are important in each play?
    50 xp
    Sentiment changes through a play
    100 xp
    Calculating net sentiment
    100 xp
    Visualizing narrative arcs
    100 xp
  3. 3

    Analyzing TV News

    Text analysis using tidy principles can be applied to diverse kinds of text, and in this chapter, you will explore a dataset of closed captioning from television news. You will apply the skills you have learned so far to explore how different stations report on a topic with different words, and how sentiment changes with time.

    Play Chapter Now
    That's the way it is
    50 xp
    Tidying TV news
    100 xp
    Counting totals
    100 xp
    Sentiment analysis of TV news
    100 xp
    Comparing TV stations
    50 xp
    Which station uses the most positive or negative words?
    100 xp
    Which words contribute to the sentiment scores?
    100 xp
    Word choice and TV station
    100 xp
    Sentiment changes with time
    50 xp
    Visualizing sentiment over time
    100 xp
    Word changes over time
    100 xp
  4. 4

    Singing a Happy Song (or Sad?!)

    In this final chapter on sentiment analysis using tidy principles, you will explore pop song lyrics that have topped the charts from the 1960s to today. You will apply all the techniques we have explored together so far, and use linear modeling to find what the sentiment of song lyrics can predict.

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    Ranking pop songs through the years
    50 xp
    Tidying song lyrics
    100 xp
    Calculating total words per song
    100 xp
    Sentiment analysis on song lyrics
    100 xp
    The most positive and negative songs
    100 xp
    Connecting sentiment to other quantities
    50 xp
    Sentiment and Billboard rank
    100 xp
    More on Billboard rank and sentiment scores
    100 xp
    Moving from song rank to year
    50 xp
    Sentiment scores by year
    100 xp
    Modeling negative sentiment
    100 xp
    Modeling positive sentiment
    100 xp
    How is sentiment in pop songs changing over the decades?
    50 xp
    Wrapping up
    50 xp
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GroupTraining 2 or more people?

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datasets

Text spoken on TV news programsGeocoded Twitter dataSix of Shakespeare's playsLyrics from pop songs over the last 50 years

collaborators

Collaborator's avatar
Richie Cotton

prerequisites

Introduction to Data Visualization with ggplot2Introduction to the Tidyverse
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