Chapter 46 Relationship among words

Til now, we have explored the most important words for a character. Now, we will explore the relationship between words.

count_bigrams <- function(dataset) {
  dataset %>%
    unnest_tokens(bigram, normalized_text, token = "ngrams", n = 2) %>%
    separate(bigram, c("word1", "word2"), sep = " ") %>%
    filter(!word1 %in% stop_words$word,
           !word2 %in% stop_words$word) %>%
    dplyr::count(word1, word2, sort = TRUE)
}


visualize_bigrams <- function(bigrams) {
  set.seed(2016)
  a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
  
  bigrams %>%
    graph_from_data_frame() %>%
    ggraph(layout = "fr") +
    geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a) +
    geom_node_point(color = "lightblue", size = 5) +
    geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
    theme_void()
  
}

visualize_bigrams_individual <- function(bigrams) {
  set.seed(2016)
  a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
  
  bigrams %>%
    graph_from_data_frame() %>%
    ggraph(layout = "fr") +
    geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a,end_cap = circle(.07, 'inches')) +
    geom_node_point(color = "lightblue", size = 5) +
    geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
    theme_void()
}

SCWords <- SC %>%
  count_bigrams()

SCWords %>%
  filter(n > 50) %>%
  visualize_bigrams()

The above infographic shows the words which follow another word. E.g. the word dont is preceded by many words such as

• care
  
• forget
  
• understand
  
• worry
  
• mind
  
• wanna

There are certain words which follows itself such as bye , hee since we have script lines which would be bye bye and hee hee respectively.

46.1 Dont word network graph

individual_words_bigrams <- function(SC, word1Value, word2Value) {
  x_Words1 <- SC %>%
    count_bigrams() %>%
    filter(word1 == word1Value)
  
  x_Words2 <- SC %>%
    count_bigrams() %>%
    filter(word2 == word2Value)
  
  x_full = rbind(x_Words1,x_Words2)
}


individual_words_bigrams(SC,"dont","dont") %>%
  filter(n > 20) %>%
  visualize_bigrams_individual()