1. Aprender
    2. /
  2. Cursos
    3. /
  3. Sentiment Analysis in R
  • 1

    Fast & Dirty: Polarity scoring

    Gratuito

    In the first chapter, you will learn how to apply qdap's sentiment function called polarity() .

  • 2

    Sentiment Analysis the tidytext Way

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

  • 3

    Visualizing Sentiment

    Make compelling visuals with your sentiment output.

  • 4

    Case study: Airbnb reviews

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

Initializing

Exercicio

Create a Tidy Text Tibble!

Since you learned about tidy principles this code helps you organize your data into a tibble so you can then work within the tidyverse!

Previously you learned that applying tidy() on a TermDocumentMatrix() object will convert the TDM to a tibble. In this exercise you will create the word data directly from the review column called comments.

First you use unnest_tokens() to make the text lowercase and tokenize the reviews into single words.

Sometimes it is useful to capture the original word order within each group of a corpus. To do so, use mutate(). In mutate() you will use seq_along() to create a sequence of numbers from 1 to the length of the object. This will capture the word order as it was written.

In the tm package, you would use removeWords() to remove stopwords. In the tidyverse you first need to load the stop words lexicon and then apply an anti_join() between the tidy text data frame and the stopwords.

Instruções

100 XP
  • Create tidy_reviews by piping (%>%) the original reviews object bos_reviews to the unnest_tokens() function. Pass in a new column name, word and declare the comments column. Remember in the tidyverse you don't need a $ or quotes.
  • Create a new variable the tidy way! Rewrite tidy_reviews by piping tidy_reviews to group_by with the column id. Then %>% it again to mutate(). Within mutate create a new variable original_word_order equal to seq_along(word).
  • Print out the tibble, tidy_reviews.
  • Load the premade "SMART" stopwords to your R session with data("stop_words").
  • Overwrite tidy_reviews by passing the original tidy_reviews to anti_join() with a %>%. Within anti_join() pass in the predetermined stop_words lexicon.