SentimentDictionaryWeighted {SentimentAnalysis} | R Documentation |
Create a sentiment dictionary of words linked to a score
Description
This routine creates a new object of type SentimentDictionaryWeighted
that
contains a number of words, each linked to a continuous score (i.e. weight) for
specifying its polarity. The scores can later be interpreted as a linear model
Usage
SentimentDictionaryWeighted(
words,
scores,
idf = rep(1, length(words)),
intercept = 0
)
Arguments
words |
is collection (vector) of different words as strings |
scores |
are the corresponding scores or weights denoting the word's polarity |
idf |
provide further details on the frequency of words in the corpus as an additional source for normalization |
intercept |
is an optional parameter for shifting the zero level (default: 0) |
Value
Returns a new object of type SentimentDictionaryWordlist
Note
The intercept is useful when the mean or median of a response variable is not exactly located at zero. For instance, stock market returns have slight positive bias.
Source
doi:10.1371/journal.pone.0209323
References
Pr\"ollochs and Feuerriegel (2018). Statistical inferences for Polarity Identification in Natural Language, PloS One 13(12).
See Also
Examples
# generate dictionary (based on linear model)
d <- SentimentDictionaryWeighted(c("increase", "decrease", "exit"),
c(+1, -1, -10),
rep(NA, 3))
summary(d)
# alternative call
d <- SentimentDictionaryWeighted(c("increase", "decrease", "exit"),
c(+1, -1, -10))
summary(d)
# alternative call
d <- SentimentDictionary(c("increase", "decrease", "exit"),
c(+1, -1, -10),
rep(NA, 3))
summary(d)