FP_sentiments.R

library(tidyverse)
library(lubridate)
library(stringr)
library(tidytext)
library(broom)
library(scales)
library(dplyr)
library(ggplot2)
library(rsconnect)

theme_set(theme_bw())

# get text
Livy <- read_csv("All_Livy.csv")%>%
  mutate(Book = parse_number(Book))

# convert to tokens
Livy <- Livy %>%
  group_by(Book, Chapter) %>%
  unnest_tokens(word, Text) %>%
  mutate(linenumber = row_number())

# remove stop words
cleaned_livy <- Livy %>%
  anti_join(stop_words)

cleaned_livy %>%
  count(word, sort = TRUE)

# add sentiment from NRC dictionary
LivySentiment <- cleaned_livy %>%
  inner_join(get_sentiments("nrc"))

# summarize sentiment by chapter
LivySentimentCount <- LivySentiment %>%
  count(Book, Chapter, sentiment) %>%
  left_join(cleaned_livy %>%
              count(Book, Chapter) %>%
              rename(n_total = n)) %>%
  mutate(pct = n / n_total)

chapter_id <- Livy %>%
  select(Book, Chapter) %>%
  distinct() %>%
  ungroup %>%
  mutate(livy_id = row_number())

LivySentimentCount <- LivySentimentCount %>%
  left_join(chapter_id) %>%
  arrange(livy_id, sentiment)

break_points <- chapter_id %>%
  group_by(Book) %>%
  slice(1) %>%
  na.omit()
  

ggplot(LivySentimentCount, aes(livy_id, pct, group = sentiment,
                               color = sentiment)) +
  geom_smooth(se = FALSE)+
  ggtitle("Sentiments In Livy")+
  xlab("Book")+
  ylab("Percent of Text per Chapter")+
  scale_x_continuous(breaks = break_points$livy_id,
                     labels = break_points$Book)