Sentiment Analysis of Deathly Hallow.Rmd

---
title: "Sentiment Analysis of The Deathly Hallow by J.K. Rowling"
by: Nipunjeet Gujral
---

### Setup
```{r library, message=FALSE, warning=FALSE}
library(harrypotter)
library(tidyverse)
library(tidytext)

titles <- c("Philosopher's Stone", "Chamber of Secrets","Prisoner of Azkaban", "Goblet of Fire", "Order of the Phoenix", "Half-Blood Prince", "Deathly Hallows")
books <- list(philosophers_stone, chamber_of_secrets, prisoner_of_azkaban, goblet_of_fire, order_of_the_phoenix, half_blood_prince, deathly_hallows)
series <- tibble()
```


### Seperating the Harry Potter Text into Descrete Books
```{r seperating books, message=FALSE, warning=FALSE}
for(i in length(titles)) {
        
        clean <- tibble(chapter = seq_along(books[[i]]),
                text = books[[i]]) %>%
             		unnest_tokens(word, text) %>%
             		mutate(book = titles[i]) %>%
             		select(book, everything())

        series <- rbind(series, clean)
}
series$book <- factor(series$book, levels = rev(titles))

```


### Plotting the Story Arc of the Deathly Hallow Using the NRC, Bing, and AFINN libraries
```{r plotting, message=FALSE, warning=FALSE}
afinn <- series %>%
        group_by(book) %>% 
        mutate(word_count = 1:n(),
               index = word_count %/% 500 + 1) %>% 
        inner_join(get_sentiments("afinn")) %>%
        group_by(book, index) %>%
        summarise(sentiment = sum(score)) %>%
        mutate(method = "AFINN")

bing_and_nrc <- bind_rows(series %>%
        group_by(book) %>% 
        mutate(word_count = 1:n(),
               index = word_count %/% 500 + 1) %>% 
        inner_join(get_sentiments("bing")) %>%
        mutate(method = "Bing"),
        series %>%
        group_by(book) %>% 
        mutate(word_count = 1:n(),
               index = word_count %/% 500 + 1) %>%
        inner_join(get_sentiments("nrc") %>%
        filter(sentiment %in% c("positive", "negative"))) %>%
        mutate(method = "NRC")) %>%
        count(book, method, index = index , sentiment) %>%
        ungroup() %>%
        spread(sentiment, n, fill = 0) %>%
        mutate(sentiment = positive - negative) %>%
        select(book, index, method, sentiment)

bind_rows(afinn, bing_and_nrc) %>%
        ungroup() %>%
        mutate(book = factor(book, levels = titles)) %>%
        ggplot(aes(index, sentiment, fill = method)) +
        geom_bar(alpha = 0.8, stat = "identity", show.legend = FALSE) +
        facet_grid(book ~ method)
```