Jump right in! Visualize polarity
Sentiment analysis helps you extract an author's feelings towards a subject. This exercise will give you a taste of what's to come!
We created text_df
representing a conversation with person
and text
columns.
Use qdap
's polarity()
function to score text_df
. polarity()
will accept a single character object or data frame with a grouping variable to calculate a positive or negative score.
In this example you will use the magrittr
package's dollar pipe operator %$%
. The dollar sign forwards the data frame into polarity()
and you declare a text column name or the text column and a grouping variable without quotes.
text_data_frame %$% polarity(text_column_name)
To create an object with the dollar sign operator:
polarity_object <- text_data_frame %$%
polarity(text_column_name, grouping_column_name)
More specifically, to make a quantitative judgement about the sentiment of some text, you need to give it a score. A simple method is a positive or negative value related to a sentence, passage or a collection of documents called a corpus. Scoring with positive or negative values only is called "polarity." A useful function for extracting polarity scores is counts()
applied to the polarity object. For a quick visual call plot()
on the polarity()
outcome.
This exercise is part of the course
Sentiment Analysis in R
Exercise instructions
- Examine the
text_df
conversation data frame. - Using
%$%
passtext_df
topolarity()
along with the column nametext
without quotes. This will print the polarity for all text. - Create a new object
datacamp_conversation
by forwardingtext_df
with%$%
topolarity()
. Pass intext
followed by the groupingperson
column. This will calculate polarity according to each individual person. Since it is all within parentheses the result will be printed too. - Apply
counts()
todatacamp_conversation
to print the specific emotional words that were found. plot()
thedatacamp_conversation
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Examine the text data
text_df
# Calc overall polarity score
text_df %$% polarity(___)
# Calc polarity score by person
(datacamp_conversation <- text_df %$% ___(___, ___))
# Counts table from datacamp_conversation
___(___)
# Plot the conversation polarity
___(___)