vader_df: Get a dataframe of vader results for multiple text documents

In krgitcode/vader: Valence Aware Dictionary and sEntiment Reasoner (VADER)

Description

Use vader_df() to calculate the valence of multiple texts contained within a vector or column in a dataframe.

Usage

vader_df(text, incl_nt = T, neu_set = T)

Arguments

text	to be analyzed; for vader_df(), the text should be a single vector (e.g. 1 column)
incl_nt	defaults to T, indicates whether you wish to incl n't contractions (e.g., can't) in negation analysis
neu_set	defaults to T, indicates whether you wish to count neutral words in calculations

Value

A dataframe 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".

N.B.

In the examples below, "yesn't" is an internet neologism meaning "no", "maybe yes, maybe no", "didn't", etc.

See Also

get_vader to get vader results for a single text document

Examples

vader_df(c("I'm happy", "I'm yesn't happy"))
vader_df(c("I'm happy", "I'm yesn't happy"), incl_nt = FALSE)
vader_df(c("I'm happy", "I'm yesn't happy"), neu_set = FALSE)

krgitcode/vader documentation built on May 12, 2020, 12:58 a.m.