R/custom.R
In SubhasreeBose/RSentiment: Analyse Sentiment of English Sentences
#' Calculate the score of sentences
#'
#' This function loads text and calculates score of each sentence on basis
#' of presence of words of positive and negative sentiment supplied externally as paramater,
#' presence of negation,and checking for sarcasm. 0 indicates neutral sentiment. Positive value indicates
#' positive sentiment. Negative value indicates negative sentiment. 99 indicates
#' sarcasm.
#' @param text A vector of sentences or a sentence (English).
#' @param positivewords A vector of words of positive sentiment.
#' @param negativewords A vector of words of negative sentiment.
#' @return A vector containing polarity of each sentence.
#' @examples
#'calculate_custom_score("This is good",c("good"),c("bad"))
#'calculate_custom_score(c("This is good","This is bad"),c("good"),c("bad"))
#'@export
calculate_custom_score <- function(text,positivewords,negativewords) {
text <- as.character(text)
#split the text by newline
# text <-unlist(lapply(text, function(x) {
# stringr::str_split(x, "\n")
# }))
check_verb <- function(r2, words, df)
{
s <- 100
if (grepl("R R V", r2) | grepl("V R V", r2))
{
onlyVerb <- df[which(df$r1 == 'V' | df$r1 == 'M'), ]
verbs <- onlyVerb$words
positive.matches <- match(verbs, positivewords)
negative.matches <- match(verbs, negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
score <- (sum(positive_matches) - sum(negative_matches))
df <- df[which(!df$r1 == 'V' & !df$r1 == 'M'), ]
words <- df$words
r2 <- paste(df$r1, collapse = " ")
s <- check_adjectives_noun(r2, words, df)
if (score < 0)
s <- (-s)
}
return(s)
}
check_adjectives_noun <- function(r2, words, df)
{
score <- 0
if (grepl("R", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R")
{
negation.matches <- match(words[i], c("not", "none", "no", "never"))
score <- (-1)
break
}
}
}
if (grepl("R J", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "J")
{
negation.matches <- match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 1], positivewords)
negative.matches <- match(df$words[i + 1], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("R R J", r2))
{
#print("Entered")
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "R" &&
substring(df$r1[i + 2], 1, 1) == "J")
{
# print("Entered")
negation.matches <-
match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 2], positivewords)
negative.matches <- match(df$words[i + 2], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("R R R", r2))
{
#print("Entered")
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "R" &&
substring(df$r1[i + 2], 1, 1) == "R")
{
#print("Entered")
negation.matches <-
match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 2], positivewords)
negative.matches <- match(df$words[i + 2], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("R R", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "R")
{
negation.matches <- match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 1], positivewords)
negative.matches <- match(df$words[i + 1], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("V N", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "V" &&
substring(df$r1[i + 1], 1, 1) == "N")
{
negation.matches <- match(words[i + 1], c("none", "nobody", "nothing"))
positiveV.matches <- match(df$words[i], positivewords)
negativeV.matches <- match(df$words[i], negativewords)
positiveN.matches <- match(df$words[i + 1], positivewords)
negativeN.matches <- match(df$words[i + 1], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positiveV_matches <- !is.na(positiveV.matches)
negativeV_matches <- !is.na(negativeV.matches)
negation_matches <- !is.na(negation.matches)
positiveN_matches <- !is.na(positiveN.matches)
negativeN_matches <- !is.na(negativeN.matches)
# final score
vscore <- (sum(positiveV_matches) - sum(negativeV_matches))
nscore <- (sum(positiveN_matches) - sum(negativeN_matches))
if (nscore == 0)
score <- vscore
else
{
if (vscore < 0)
score <- (-nscore)
else
score <- nscore
}
if (negation_matches > 0)
score <- (-score)
break
}
}
}
return(score)
}
#function to tag parts of speech of each sentence
POStag <- function(x, words)
{
type <- ""
sent_token_annotator <- openNLP::Maxent_Sent_Token_Annotator()
word_token_annotator <- openNLP::Maxent_Word_Token_Annotator()
pos_tag_annotator <- openNLP::Maxent_POS_Tag_Annotator()
y1 <-NLP::annotate(x, list(sent_token_annotator, word_token_annotator))
y2 <- NLP::annotate(x, pos_tag_annotator, y1)
y2w <- subset(y2, type == "word")
tags <- sapply(y2w$features, '[[', "POS")
r1 <- sprintf("%s", tags)
for (i in 1:length(r1))
{
r1[i] <- substring(r1[i], 1, 1)
}
df <- data.frame(r1, words)
df <- df[!df$r1 == 'D', ]
words <- df$words
r2 <- paste(df$r1, collapse = " ")
score_adj <- check_adjectives_noun(r2, words, df)
score_verb <- check_verb(r2, words, df)
if (score_verb == 100)
return (score_adj)
else
return(score_verb)
}
#function to calculate number of words in each category within a sentence
getpolarity <- function(sentences,
negativewords,
positivewords) {
negation <- c("no", "not", "none", "nobody", "nothing", "never")
polaritys <-
plyr::laply(sentences, function(sentence,
negativewords,
positivewords) {
if (is.na(sentence))
return(NA)
#checking emoticons
if (regexpr("[?]", sentence) > 0)
return(99)
if (grepl(":-(", sentence, fixed = TRUE))
sentence <- paste(sentence, "bad", sep = " ")
else if (grepl(":-)", sentence, fixed = TRUE) ||
grepl(":)", sentence, fixed = TRUE))
sentence <- paste(sentence, "good", sep = " ")
else if (grepl(":-D", sentence, fixed = TRUE))
sentence <- paste(sentence, "very good", sep = " ")
else if (grepl(":-|", sentence, fixed = TRUE))
sentence <- paste(sentence, "indifferent", sep = " ")
else if (grepl(":-X", sentence, fixed = TRUE))
sentence <- paste(sentence, "very bad", sep = " ")
else
sentence <- paste(sentence, "", sep = "")
sentence <- iconv(sentence, "WINDOWS-1252", "UTF-8")
#remove unnecessary characters and split up by word
trim <- function (x)
gsub("^\\s+|\\s+$", "", x)
#n't is equivalent to not
sentence <- gsub("n't", " not", sentence)
sentence <- trim(sentence)
sentence <- gsub('[[:punct:]]', '', iconv(sentence, to = "ASCII//TRANSLIT"))
sentence <- gsub('[[:cntrl:]]', '', iconv(sentence, to = "ASCII//TRANSLIT"))
sentence<-stringr::str_trim(iconv(sentence, to = "ASCII//TRANSLIT"))
#sentence<-gsub("[[:punct:]]","",iconv(sentence, to = "ASCII//TRANSLIT"))
sentence <- tolower(sentence)
wordList <- stringr::str_split(sentence, '\\s+')
words <- unlist(wordList)
#build vector with matches between sentence and each category
positive.matches <- match(words, positivewords)
negative.matches <- match(words, negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
# final score
score <- sum(positive_matches) - sum(negative_matches)
very.matches <- match(words, c("very", "most", "more"))
very_matches <- !is.na(very.matches)
if (score >= 0)
score <- score + sum(very_matches)
else
score <- score - sum(very_matches)
negation.matches <- match(words, negation)
negation_matches <- !is.na(negation.matches)
print (paste("Processing sentence:", sentence, sep=" "))
if (sum(negation_matches) > 0)
score <- POStag(sentence, words)
return(score)
}, negativewords, positivewords)
return(polaritys)
}
negativewords <- iconv(negativewords, "WINDOWS-1252", "UTF-8")
positivewords <- iconv(positivewords, "WINDOWS-1252", "UTF-8")
#build tables of positive and negative sentences with polaritys
negativewords <- tolower(negativewords)
positivewords <- tolower(positivewords)
res <- getpolarity(text, negativewords, positivewords)
return (res)
}
#' Predicts the sentiment of sentences
#'
#' This function loads text and words of positive and negative sentiment supplied externally as paramater and calculates sentiment of each sentence. It classifies
#' sentences into 6 categories: Positive, Negative, Very Positive, Very Negative
#' Sarcasm and Neutral.
#'
#' @param text A vector of sentences or a sentence (English).
#' @param positivewords A vector of words of positive sentiment.
#' @param negativewords A vector of words of negative sentiment.
#' @return A vector containing sentiment of each sentence.
#' @examples
#'calculate_custom_sentiment("This is good",c("good"),c("bad"))
#'calculate_custom_sentiment(c("This is good","This is bad"),c("good"),c("bad"))
#'@export
calculate_custom_sentiment <- function(text,positivewords,negativewords)
{
res <- calculate_custom_score(text,positivewords,negativewords)
sentiment <- c()
for (i in 1:length(res))
{
if (res[i] == 0)
{
sentiment[i] <- 'Neutral'
}
else if (res[i] == -1) {
sentiment[i] <- 'Negative'
}
else if (res[i] == 1) {
sentiment[i] <- 'Positive'
}
else if (res[i] > 1) {
sentiment[i] <- 'Very Positive'
}
else if (res[i] == 99)
{
sentiment[i] <- 'Sarcasm'
}
else{
sentiment[i] <- 'Very Negative'
}
}
results <- data.frame(text, sentiment)
return (results)
}
#' Calculate the number of sentences in each category of sentiment.
#'
#' This function loads text and words of positive and negative sentiment supplied externally as paramater, and calculates number of sentences which are positive,
#' negative, very positive, very negative, neutral and sarcasm.
#'
#' @param text A vector of sentences or a sentence (English).
#' @param positivewords A vector of words of positive sentiment.
#' @param negativewords A vector of words of negative sentiment.
#' @return A 2-D matrix with two rows and 6 columns where first row contains the name of sentiment
#' category and the second row contains the number in each category in string format.
#' @examples
#'calculate_custom_total_presence_sentiment(c("This is good","This is bad"),c("good"),c("bad"))
#'@export
calculate_custom_total_presence_sentiment <- function(text,positivewords, negativewords) {
res <- calculate_custom_score(text,positivewords, negativewords)
score_array <- array(0, dim = c(2, 6))
score_array[1, 1] <- 'Sarcasm'
score_array[1, 2] <- 'Negative'
score_array[1, 3] <- 'Very Negative'
score_array[1, 4] <- 'Neutral'
score_array[1, 5] <- 'Positive'
score_array[1, 6] <- 'Very Positive'
for (i in 1:length(res))
{
if (res[i] == 99)
{
score_array[2, 1] <- as.numeric(score_array[2, 1]) + 1
}
else if (res[i] == 0)
{
score_array[2, 4] <- as.numeric(score_array[2, 4]) + 1
}
else if (res[i] == -1) {
score_array[2, 2] <- as.numeric(score_array[2, 2]) + 1
}
else if (res[i] == 1) {
score_array[2, 5] <- as.numeric(score_array[2, 5]) + 1
}
else if (res[i] > 1) {
score_array[2, 6] <- as.numeric(score_array[2, 6]) + 1
}
else{
score_array[2, 3] <- as.numeric(score_array[2, 3]) + 1
}
}
return (score_array)
}
SubhasreeBose/RSentiment documentation built on May 20, 2019, 5:09 p.m.
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#' Calculate the score of sentences
#'
#' This function loads text and calculates score of each sentence on basis
#' of presence of words of positive and negative sentiment supplied externally as paramater,
#' presence of negation,and checking for sarcasm. 0 indicates neutral sentiment. Positive value indicates
#' positive sentiment. Negative value indicates negative sentiment. 99 indicates
#' sarcasm.
#' @param text A vector of sentences or a sentence (English).
#' @param positivewords A vector of words of positive sentiment.
#' @param negativewords A vector of words of negative sentiment.
#' @return A vector containing polarity of each sentence.
#' @examples
#'calculate_custom_score("This is good",c("good"),c("bad"))
#'calculate_custom_score(c("This is good","This is bad"),c("good"),c("bad"))
#'@export
calculate_custom_score <- function(text,positivewords,negativewords) {
text <- as.character(text)
#split the text by newline
# text <-unlist(lapply(text, function(x) {
# stringr::str_split(x, "\n")
# }))
check_verb <- function(r2, words, df)
{
s <- 100
if (grepl("R R V", r2) | grepl("V R V", r2))
{
onlyVerb <- df[which(df$r1 == 'V' | df$r1 == 'M'), ]
verbs <- onlyVerb$words
positive.matches <- match(verbs, positivewords)
negative.matches <- match(verbs, negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
score <- (sum(positive_matches) - sum(negative_matches))
df <- df[which(!df$r1 == 'V' & !df$r1 == 'M'), ]
words <- df$words
r2 <- paste(df$r1, collapse = " ")
s <- check_adjectives_noun(r2, words, df)
if (score < 0)
s <- (-s)
}
return(s)
}
check_adjectives_noun <- function(r2, words, df)
{
score <- 0
if (grepl("R", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R")
{
negation.matches <- match(words[i], c("not", "none", "no", "never"))
score <- (-1)
break
}
}
}
if (grepl("R J", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "J")
{
negation.matches <- match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 1], positivewords)
negative.matches <- match(df$words[i + 1], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("R R J", r2))
{
#print("Entered")
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "R" &&
substring(df$r1[i + 2], 1, 1) == "J")
{
# print("Entered")
negation.matches <-
match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 2], positivewords)
negative.matches <- match(df$words[i + 2], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("R R R", r2))
{
#print("Entered")
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "R" &&
substring(df$r1[i + 2], 1, 1) == "R")
{
#print("Entered")
negation.matches <-
match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 2], positivewords)
negative.matches <- match(df$words[i + 2], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("R R", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "R" &&
substring(df$r1[i + 1], 1, 1) == "R")
{
negation.matches <- match(words[i], c("not", "none", "no", "never"))
positive.matches <- match(df$words[i + 1], positivewords)
negative.matches <- match(df$words[i + 1], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
negation_matches <- !is.na(negation.matches)
# final score
score <- (sum(positive_matches) - sum(negative_matches))
if (negation_matches > 0)
score <- (-score)
break
}
}
}
if (grepl("V N", r2))
{
for (i in 1:nrow(df))
{
if (substring(df$r1[i], 1, 1) == "V" &&
substring(df$r1[i + 1], 1, 1) == "N")
{
negation.matches <- match(words[i + 1], c("none", "nobody", "nothing"))
positiveV.matches <- match(df$words[i], positivewords)
negativeV.matches <- match(df$words[i], negativewords)
positiveN.matches <- match(df$words[i + 1], positivewords)
negativeN.matches <- match(df$words[i + 1], negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positiveV_matches <- !is.na(positiveV.matches)
negativeV_matches <- !is.na(negativeV.matches)
negation_matches <- !is.na(negation.matches)
positiveN_matches <- !is.na(positiveN.matches)
negativeN_matches <- !is.na(negativeN.matches)
# final score
vscore <- (sum(positiveV_matches) - sum(negativeV_matches))
nscore <- (sum(positiveN_matches) - sum(negativeN_matches))
if (nscore == 0)
score <- vscore
else
{
if (vscore < 0)
score <- (-nscore)
else
score <- nscore
}
if (negation_matches > 0)
score <- (-score)
break
}
}
}
return(score)
}
#function to tag parts of speech of each sentence
POStag <- function(x, words)
{
type <- ""
sent_token_annotator <- openNLP::Maxent_Sent_Token_Annotator()
word_token_annotator <- openNLP::Maxent_Word_Token_Annotator()
pos_tag_annotator <- openNLP::Maxent_POS_Tag_Annotator()
y1 <-NLP::annotate(x, list(sent_token_annotator, word_token_annotator))
y2 <- NLP::annotate(x, pos_tag_annotator, y1)
y2w <- subset(y2, type == "word")
tags <- sapply(y2w$features, '[[', "POS")
r1 <- sprintf("%s", tags)
for (i in 1:length(r1))
{
r1[i] <- substring(r1[i], 1, 1)
}
df <- data.frame(r1, words)
df <- df[!df$r1 == 'D', ]
words <- df$words
r2 <- paste(df$r1, collapse = " ")
score_adj <- check_adjectives_noun(r2, words, df)
score_verb <- check_verb(r2, words, df)
if (score_verb == 100)
return (score_adj)
else
return(score_verb)
}
#function to calculate number of words in each category within a sentence
getpolarity <- function(sentences,
negativewords,
positivewords) {
negation <- c("no", "not", "none", "nobody", "nothing", "never")
polaritys <-
plyr::laply(sentences, function(sentence,
negativewords,
positivewords) {
if (is.na(sentence))
return(NA)
#checking emoticons
if (regexpr("[?]", sentence) > 0)
return(99)
if (grepl(":-(", sentence, fixed = TRUE))
sentence <- paste(sentence, "bad", sep = " ")
else if (grepl(":-)", sentence, fixed = TRUE) ||
grepl(":)", sentence, fixed = TRUE))
sentence <- paste(sentence, "good", sep = " ")
else if (grepl(":-D", sentence, fixed = TRUE))
sentence <- paste(sentence, "very good", sep = " ")
else if (grepl(":-|", sentence, fixed = TRUE))
sentence <- paste(sentence, "indifferent", sep = " ")
else if (grepl(":-X", sentence, fixed = TRUE))
sentence <- paste(sentence, "very bad", sep = " ")
else
sentence <- paste(sentence, "", sep = "")
sentence <- iconv(sentence, "WINDOWS-1252", "UTF-8")
#remove unnecessary characters and split up by word
trim <- function (x)
gsub("^\\s+|\\s+$", "", x)
#n't is equivalent to not
sentence <- gsub("n't", " not", sentence)
sentence <- trim(sentence)
sentence <- gsub('[[:punct:]]', '', iconv(sentence, to = "ASCII//TRANSLIT"))
sentence <- gsub('[[:cntrl:]]', '', iconv(sentence, to = "ASCII//TRANSLIT"))
sentence<-stringr::str_trim(iconv(sentence, to = "ASCII//TRANSLIT"))
#sentence<-gsub("[[:punct:]]","",iconv(sentence, to = "ASCII//TRANSLIT"))
sentence <- tolower(sentence)
wordList <- stringr::str_split(sentence, '\\s+')
words <- unlist(wordList)
#build vector with matches between sentence and each category
positive.matches <- match(words, positivewords)
negative.matches <- match(words, negativewords)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
positive_matches <- !is.na(positive.matches)
negative_matches <- !is.na(negative.matches)
# final score
score <- sum(positive_matches) - sum(negative_matches)
very.matches <- match(words, c("very", "most", "more"))
very_matches <- !is.na(very.matches)
if (score >= 0)
score <- score + sum(very_matches)
else
score <- score - sum(very_matches)
negation.matches <- match(words, negation)
negation_matches <- !is.na(negation.matches)
print (paste("Processing sentence:", sentence, sep=" "))
if (sum(negation_matches) > 0)
score <- POStag(sentence, words)
return(score)
}, negativewords, positivewords)
return(polaritys)
}
negativewords <- iconv(negativewords, "WINDOWS-1252", "UTF-8")
positivewords <- iconv(positivewords, "WINDOWS-1252", "UTF-8")
#build tables of positive and negative sentences with polaritys
negativewords <- tolower(negativewords)
positivewords <- tolower(positivewords)
res <- getpolarity(text, negativewords, positivewords)
return (res)
}
#' Predicts the sentiment of sentences
#'
#' This function loads text and words of positive and negative sentiment supplied externally as paramater and calculates sentiment of each sentence. It classifies
#' sentences into 6 categories: Positive, Negative, Very Positive, Very Negative
#' Sarcasm and Neutral.
#'
#' @param text A vector of sentences or a sentence (English).
#' @param positivewords A vector of words of positive sentiment.
#' @param negativewords A vector of words of negative sentiment.
#' @return A vector containing sentiment of each sentence.
#' @examples
#'calculate_custom_sentiment("This is good",c("good"),c("bad"))
#'calculate_custom_sentiment(c("This is good","This is bad"),c("good"),c("bad"))
#'@export
calculate_custom_sentiment <- function(text,positivewords,negativewords)
{
res <- calculate_custom_score(text,positivewords,negativewords)
sentiment <- c()
for (i in 1:length(res))
{
if (res[i] == 0)
{
sentiment[i] <- 'Neutral'
}
else if (res[i] == -1) {
sentiment[i] <- 'Negative'
}
else if (res[i] == 1) {
sentiment[i] <- 'Positive'
}
else if (res[i] > 1) {
sentiment[i] <- 'Very Positive'
}
else if (res[i] == 99)
{
sentiment[i] <- 'Sarcasm'
}
else{
sentiment[i] <- 'Very Negative'
}
}
results <- data.frame(text, sentiment)
return (results)
}
#' Calculate the number of sentences in each category of sentiment.
#'
#' This function loads text and words of positive and negative sentiment supplied externally as paramater, and calculates number of sentences which are positive,
#' negative, very positive, very negative, neutral and sarcasm.
#'
#' @param text A vector of sentences or a sentence (English).
#' @param positivewords A vector of words of positive sentiment.
#' @param negativewords A vector of words of negative sentiment.
#' @return A 2-D matrix with two rows and 6 columns where first row contains the name of sentiment
#' category and the second row contains the number in each category in string format.
#' @examples
#'calculate_custom_total_presence_sentiment(c("This is good","This is bad"),c("good"),c("bad"))
#'@export
calculate_custom_total_presence_sentiment <- function(text,positivewords, negativewords) {
res <- calculate_custom_score(text,positivewords, negativewords)
score_array <- array(0, dim = c(2, 6))
score_array[1, 1] <- 'Sarcasm'
score_array[1, 2] <- 'Negative'
score_array[1, 3] <- 'Very Negative'
score_array[1, 4] <- 'Neutral'
score_array[1, 5] <- 'Positive'
score_array[1, 6] <- 'Very Positive'
for (i in 1:length(res))
{
if (res[i] == 99)
{
score_array[2, 1] <- as.numeric(score_array[2, 1]) + 1
}
else if (res[i] == 0)
{
score_array[2, 4] <- as.numeric(score_array[2, 4]) + 1
}
else if (res[i] == -1) {
score_array[2, 2] <- as.numeric(score_array[2, 2]) + 1
}
else if (res[i] == 1) {
score_array[2, 5] <- as.numeric(score_array[2, 5]) + 1
}
else if (res[i] > 1) {
score_array[2, 6] <- as.numeric(score_array[2, 6]) + 1
}
else{
score_array[2, 3] <- as.numeric(score_array[2, 3]) + 1
}
}
return (score_array)
}
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