Description Usage Arguments Value References N.B. See Also Examples
View source: R/vader_main_call.R
Use get_vader() to calculate the valence of a single text document.
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text |
to be analyzed; for get_vader(), the text should be a character string |
incl_nt |
defaults to T, indicates whether you wish to incl UNUSUAL n't contractions (e.g., yesn't) in negation analysis |
neu_set |
defaults to T, indicates whether you wish to count neutral words in calculations |
rm_qm |
defaults to T, indicates whether you wish to clean quotation marks from text (setting to F may result in errors) |
A named vector containing the valence score for each word; an overall, compound valence score for the text; the weighted percentage of positive, negative, and neutral words in the text; and the frequency of the word "but".
For the original Python Code, please see:
https://github.com/cjhutto/vaderSentiment
https://github.com/cjhutto/vaderSentiment/blob/master/vaderSentiment/vaderSentiment.py
For the original R Code, please see:
https://github.com/nrguimaraes/sentimentSetsR/blob/master/R/ruleBasedSentimentFunctions.R
Modifications to the above scripts include, but are not limited to:
ALL CAPS fx: updated to account for non-alpha words; i.e. "I'M 100 PERCENT SURE" would previously have been counted as mixed case due to the use of numbers
IDIOMS fx: added capacity to check for idioms that do not contain any words found in the Vader Lexicon
WORDS+EMOT: strip punctuation while preserving ALL emoticons found in dictionary
Option to turn on/off neutral count
In the examples below, "yesn't" is an internet neologism meaning "no", "maybe yes, maybe no", "didn't", etc.
vader_df
to get vader results for multiple text documents
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