All sentiment analysis have been performed and a word cloud has been created for emotions.

# Installation of packages

install.packages("stringr")

install.packages("syuzhet")

install.packages("NLP")

install.packages("tm")

install.packages("wordcloud")

install.packages("RColorBrewer")

# Loading of packages

library(stringr)

library(syuzhet)

library(NLP)

library(tm)

library(wordcloud)

library(RColorBrewer)

#loading necessary dataset

setwd("D:/Hohenheim/SEM 3/ADS/introads_ass2_team18/01_data")

# Labeling the sentiment score with sentiment

rev.sentiment$sentiment.label <- ifelse(rev.sentiment$sentiment.score == 1, "Positive",

                                        ifelse(rev.sentiment$sentiment.score == -1, "Negative", "Neutral"))

# Counting the total number reviews:

positive.counts <- sum(rev.sentiment$sentiment.label == "Positive")

cat("The total number of positive reviews are", positive.counts)

negative.counts <- sum(rev.sentiment$sentiment.label == "Negative")

cat("The total number of negative reviews are", negative.counts)

neutral.counts <- sum(rev.sentiment$sentiment.label == "Neutral")

cat("The total number of neutral reviews are", neutral.counts)

# Syuzhet sentiment analysis using bing dictionary------------------------------

# Sentiment analysis using the Bing dictionary

rev.sentiment$score.syuzhet <- get_sentiment(rev.sentiment$reviews, method = "bing")

# Labeling the sentiment score with sentiment

rev.sentiment$sent.label.syuzhet <- ifelse(rev.sentiment$score.syuzhet >= 1, "Positive",

                                        ifelse(rev.sentiment$score.syuzhet <= -1, "Negative", "Neutral"))

# Sentiment scores by manually and with syuzhet package and chceking if its identical or not

rev.sentiment$identical.score <- ifelse(rev.sentiment$sentiment.score == score.syuzhet, 1 , 0)

# Calculate the percentage of identical scores

percentage.identical <- mean(rev.sentiment$identical.score) * 100

# Display the result

cat("Percentage of Identical Score:", percentage.identical, "%\n")

# Sentiment analysis by using other two dictionaries from syuhet package--------

# sentiment score using AFINN dictionary

rev.sentiment$score.afinn <- get_sentiment(rev.sentiment$reviews, method = "afinn")

# Labeling sentiment score with sentiment

rev.sentiment$sent.label.afinn <- ifelse(rev.sentiment$score.afinn >= 1, "Positive",

                                         ifelse(rev.sentiment$score.afinn <= -1, "Negative", "Neutral"))

# Sentiment score using nrc dictionary

rev.sentiment$score.nrc <- get_nrc_sentiment(rev.sentiment$reviews)

# Wordcloud based on emotions---------------------------------------------------

reviews <- rev.sentiment$reviews

# Extract the NRC emotion scores for each term

# Assuming 'nrc_scores' is a column containing NRC scores for each review in your rev.sentiment dataframe

nrc_scores <- rev.sentiment$score.nrc

#Sum the NRC scores for each emotion across all reviews

aggregate_nrc <- colSums(nrc_scores, na.rm = TRUE)

# Convert the aggregated scores to a data frame

nrc_aggregated_df <- data.frame(emotion = names(aggregate_nrc), score = aggregate_nrc)

# Remove rows with NAs (if any)

nrc_aggregated_df <- nrc_aggregated_df[complete.cases(nrc_aggregated_df), ]

# Create the word cloud

wordcloud(words = nrc_aggregated_df$emotion, freq = nrc_aggregated_df$score, 

          min.freq = 1, scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))