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Title: Valence Aware Dictionary and sEntiment Reasoner (VADER)
Version: 0.2.1
Description: A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. Hutto & Gilbert (2014) https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109/8122.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: tm
Depends: R (≥ 2.10)
Suggests: spelling
Language: en-US
NeedsCompilation: no
Packaged: 2020-09-07 13:59:57 UTC; kr
Author: Katherine Roehrick [aut, cre]
Maintainer: Katherine Roehrick <kr.gitcode@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-07 14:20:03 UTC

Get a named vector of vader results for a single text document

Description

Use get_vader() to calculate the valence of a single text document.

Usage

get_vader(text, incl_nt = T, neu_set = T, rm_qm = T)

Arguments

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)

Value

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".

References

For the original Python Code, please see:

For the original R Code, please see:

Modifications to the above scripts include, but are not limited to:

N.B.

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

See Also

vader_df to get vader results for multiple text documents

Examples

get_vader("I yesn't like it")
get_vader("I yesn't like it", incl_nt = FALSE)
get_vader("I yesn't like it", neu_set = FALSE)
get_vader("I said \"I'm not happy\"", rm_qm = FALSE)
get_vader("I said \" I'm not happy \" ", rm_qm = FALSE)


Get a dataframe of vader results for multiple text documents

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, rm_qm = F)

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 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)

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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