Author : tmlab / Date : 2016. 10. 27. 18:04 / Category : Text Mining/R
#install.packages("devtools")
#devtools::install_github("kbenoit/quanteda")
library(devtools)
library(quanteda)
## quanteda version 0.9.5.23
##
## Attaching package: 'quanteda'
## The following object is masked from 'package:base':
##
## sample
mfdict <- dictionary(file = "http://ow.ly/VMRkL", format = "LIWC")
## Warning in lapply(catlist[2:ncol(catlist)], as.integer): 강제형변환에 의해
## 생성된 NA 입니다
## Warning in lapply(catlist[2:ncol(catlist)], as.integer): 강제형변환에 의해
## 생성된 NA 입니다
mydfm <- dfm(inaugTexts, dictionary = mfdict)
##
## ... lowercasing
## ... tokenizing
## ... indexing documents: 57 documents
## ... indexing features: 9,215 feature types
## ... applying a dictionary consisting of 11 keys
## ... created a 57 x 11 sparse dfm
## ... complete.
## Elapsed time: 0.87 seconds.
mydfm
## Document-feature matrix of: 57 documents, 11 features.
topfeatures(mydfm, decreasing=TRUE)
## IngroupVirtue AuthorityVirtue HarmVirtue FairnessVirtue
## 1387 1039 895 567
## HarmVice MoralityGeneral IngroupVice PurityVirtue
## 405 319 226 90
## FairnessVice AuthorityVice
## 88 51
features(mydfm)
## [1] "HarmVirtue" "HarmVice" "FairnessVirtue"
## [4] "FairnessVice" "IngroupVirtue" "IngroupVice"
## [7] "AuthorityVirtue" "AuthorityVice" "PurityVirtue"
## [10] "PurityVice" "MoralityGeneral"
a=as.matrix(mydfm)
b=as.data.frame(a)
sum(b$PurityVice)
## [1] 28