NoVAD is the Norms of Valence, Arousal and Dominance (Warriner, Kuperman & Brysbaert , 2013). The function takes a string as an argument and returns the mean value of valence, arousal and dominance.

NoVAD.R

novad <- read.csv("Download_NoVAD_csv.csv") #Change for you local csv file
novad$Word <- as.character(novad$Word)
library(stringr)
library(tm)

NoVAD <- function(a){
  a <- as.character(a)
  sms_corpus <- Corpus(VectorSource(a))
  #translate all letters to lower case
  clean_corpus <- tm_map(sms_corpus, tolower)
  # remove numbers
  clean_corpus <- tm_map(clean_corpus, removeNumbers)
  # remove punctuation
  clean_corpus <- tm_map(clean_corpus, removePunctuation)
  clean_corpus <- tm_map(clean_corpus, stripWhitespace)
  
  b <- strsplit(clean_corpus[[1]]$content, " ")
  b <- unlist(b)
  sums_c <- double(length(b))
  sums_d <- double(length(b))
  sums_e <- double(length(b))
  
  
  for (i in 1:length(b)){
    M_str <- str_detect(b[i], novad$Word)==T
    M_char <- nchar(b[i])==nchar(novad$Word)
    c <- sum(novad$V.Mean.Sum[M_char*M_str==T]) #valence
    d <- sum(novad$A.Mean.Sum[M_char*M_str==T]) #arousal
    e <- sum(novad$D.Mean.Sum[M_char*M_str==T]) #dominance
    
    sums_c[i] <- c 
    sums_d[i] <- d
    sums_e[i] <- e
  }
  
  valence <- sum(sums_c)/length(sums_c[sums_c>0])
  arousal <- sum(sums_d)/length(sums_d[sums_d>0])
  dominance <- sum(sums_e)/length(sums_e[sums_e>0])
  
  return(c(valence, arousal, dominance))
}