Polarity on actual text
So far you have learned the basic components needed for assessing positive or negative intent in text. Remember the following points so you can feel confident in your results.
- The subjectivity lexicon is a predefined list of words associated with emotions or positive/negative feelings.
- You don't have to list every word in a subjectivity lexicon because Zipf's law describes human expression.
A quick way to get started is to use the polarity() function which has a built-in subjectivity lexicon.
The function scans the text to identify words in the lexicon. It then creates a cluster around an identified subjectivity word. Within the cluster valence shifters adjust the score. Valence shifters are words that amplify or negate the emotional intent of the subjectivity word. For example, "well known" is positive while "not well known" is negative. Here "not" is a negating term and reverses the emotional intent of "well known." In contrast, "very well known" employs an amplifier increasing the positive intent.
The polarity() function then calculates a score using subjectivity terms, valence shifters and the total number of words in the passage. This exercise demonstrates a simple polarity calculation. In the next video we look under the hood of polarity() for more detail.
This exercise is part of the course
Sentiment Analysis in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Example statement
positive <- "DataCamp courses are good for learning"
# Calculate polarity of statement
(pos_score <-___(___))