In this post I’ll focus on the Tidy Data principles. However, here is the Github repo with the scripts to scrap the transcripts and subtitles of Rick and Morty.
Here I’m using the subtitles of the TV show, as some of the transcripts I could scrap were incomplete.
Note: If some images appear too small on your screen you can open them in a new tab to show them in their original size.
The subtools package returns a data frame after reading srt files. In addition to that resulting data frame I wanted to explicitly point the season and chapter of each line of the subtitles. To do that I had to scrap the subtitles and then use
str_replace_all. To follow the steps clone the repo from Github:
git clone https://github.com/pachamaltese/rick_and_morty_tidy_text
Rick and Morty Can Be So Tidy
After reading the tidy file I created after scraping the subtitles, I use
unnest_tokens to divide the subtitles in words. This function uses the tokenizers package to separate each line into words. The default tokenizing is for words, but other options include characters, sentences, lines, paragraphs, or separation around a regex pattern.
if (!require("pacman")) install.packages("pacman") p_load(data.table,tidyr,stringr,tidytext,dplyr,janitor,ggplot2,viridis,ggstance,igraph) p_load_gh("thomasp85/ggraph","dgrtwo/widyr") rick_and_morty_subs = as_tibble(fread("2017-10-13_rick_and_morty_tidy_data/rick_and_morty_subs.csv")) rick_and_morty_subs_tidy = rick_and_morty_subs %>% unnest_tokens(word,text) %>% anti_join(stop_words)
The data is in one-word-per-row format, and we can manipulate it with tidy tools like dplyr. For example, in the last chunk I used an
anti_join to remove words such a “a”, “an” or “the”.
Then we can use
count to find the most common words in all of Rick and Morty episodes as a whole.
rick_and_morty_subs_tidy %>% count(word, sort = TRUE)
# A tibble: 8,100 x 2 word n
1 morty 1842 2 rick 1625 3 jerry 621 4 yeah 484 5 gonna 421 6 hey 391 7 summer 389 8 uh 331 9 time 319 10 beth 295 # ... with 8,090 more rows
Sentiment analysis can be done as an inner join. Three sentiment lexicons are in the tidytext package in the sentiment dataset. Let’s examine how sentiment changes changes during each novel. Let’s find a sentiment score for each word using the Bing lexicon, then count the number of positive and negative words in defined sections of each novel.
bing = sentiments %>% filter(lexicon == "bing") %>% select(-score) bing
# A tibble: 6,788 x 3 word sentiment lexicon
1 2-faced negative bing 2 2-faces negative bing 3 a+ positive bing 4 abnormal negative bing 5 abolish negative bing 6 abominable negative bing 7 abominably negative bing 8 abominate negative bing 9 abomination negative bing 10 abort negative bing # ... with 6,778 more rows
rick_and_morty_sentiment = rick_and_morty_subs_tidy %>% inner_join(bing) %>% count(episode_name, index = linenumber %/% 50, sentiment) %>% spread(sentiment, n, fill = 0) %>% mutate(sentiment = positive - negative) %>% left_join(rick_and_morty_subs_tidy[,c("episode_name","season","episode")] %>% distinct()) %>% arrange(season,episode) %>% mutate(episode_name = paste(season,episode,"-",episode_name), season = factor(season, labels = c("Season 1", "Season 2", "Season 3"))) %>% select(episode_name, season, everything(), -episode) rick_and_morty_sentiment
# A tibble: 431 x 6 episode_name season index negative positive sentiment
1 S01 E01 - Pilot Season 1 0 6 3 -3 2 S01 E01 - Pilot Season 1 1 10 0 -10 3 S01 E01 - Pilot Season 1 2 3 1 -2 4 S01 E01 - Pilot Season 1 3 10 4 -6 5 S01 E01 - Pilot Season 1 4 2 5 3 6 S01 E01 - Pilot Season 1 5 8 4 -4 7 S01 E01 - Pilot Season 1 6 6 1 -5 8 S01 E01 - Pilot Season 1 7 7 4 -3 9 S01 E01 - Pilot Season 1 8 14 5 -9 10 S01 E01 - Pilot Season 1 9 3 2 -1 # ... with 421 more rows
Now we can plot these sentiment scores across the plot trajectory of each novel. In the second plot I’m just showing Dan Harmon’s favourite episodes provided to the moment the show has 31 episodes in total.
ggplot(rick_and_morty_sentiment, aes(index, sentiment, fill = season)) + geom_bar(stat = "identity", show.legend = FALSE) + facet_wrap(~season, nrow = 3, scales = "free_x", dir = "v") + theme_minimal(base_size = 13) + labs(title = "Sentiment in Rick and Morty", y = "Sentiment") + scale_fill_viridis(end = 0.75, discrete=TRUE) + scale_x_discrete(expand=c(0.02,0)) + theme(strip.text=element_text(hjust=0)) + theme(strip.text = element_text(face = "italic")) + theme(axis.title.x=element_blank()) + theme(axis.ticks.x=element_blank()) + theme(axis.text.x=element_blank())
rick_and_morty_sentiment_favourites = rick_and_morty_sentiment %>% filter(grepl("S03 E03|S03 E07|S01 E06|S02 E03|S02 E07", episode_name)) ggplot(rick_and_morty_sentiment_favourites, aes(index, sentiment, fill = season)) + geom_bar(stat = "identity", show.legend = FALSE) + facet_wrap(~episode_name, ncol = 5, scales = "free_x", dir = "h") + theme_minimal(base_size = 13) + labs(title = "Sentiment in Rick and Morty\n(Creator's favourite episodes)", y = "Sentiment") + scale_fill_viridis(end = 0.75, discrete=TRUE) + scale_x_discrete(expand=c(0.02,0)) + theme(strip.text=element_text(hjust=0)) + theme(strip.text = element_text(face = "italic")) + theme(axis.title.x=element_blank()) + theme(axis.ticks.x=element_blank()) + theme(axis.text.x=element_blank())
Looking at Units Beyond Words
Lots of useful work can be done by tokenizing at the word level, but sometimes it is useful or necessary to look at different units of text. For example, some sentiment analysis algorithms look beyond only unigrams (i.e. single words) to try to understand the sentiment of a sentence as a whole. These algorithms try to understand that I am not having a good day is a negative sentence, not a positive one, because of negation.
rick_and_morty_sentences = rick_and_morty_subs %>% group_by(season) %>% unnest_tokens(sentence, text, token = "sentences") %>% ungroup()
Let’s look at just one.
 "is gonna be really liberating."
We can use tidy text analysis to ask questions such as what are the most negative episodes in each of Rick and Morty’s seasons? First, let’s get the list of negative words from the Bing lexicon. Second, let’s make a dataframe of how many words are in each chapter so we can normalize for the length of chapters. Then, let’s find the number of negative words in each chapter and divide by the total words in each chapter. Which chapter has the highest proportion of negative words?
bingnegative = sentiments %>% filter(lexicon == "bing", sentiment == "negative") wordcounts = rick_and_morty_subs_tidy %>% group_by(season, episode) %>% summarize(words = n()) rick_and_morty_subs_tidy %>% semi_join(bingnegative) %>% group_by(season, episode) %>% summarize(negativewords = n()) %>% left_join(wordcounts, by = c("season", "episode")) %>% mutate(ratio = negativewords/words) %>% top_n(1)
# A tibble: 3 x 5 # Groups: season  season episode negativewords words ratio
1 S01 E02 124 1036 0.1196911 2 S02 E01 184 1386 0.1327561 3 S03 E06 197 1486 0.1325707
Networks of Words
Another function in widyr is
pairwise_count, which counts pairs of items that occur together within a group. Let’s count the words that occur together in the lines of the first season.
rick_and_morty_words = rick_and_morty_subs_tidy %>% filter(season == "S01") word_cooccurences = rick_and_morty_words %>% pairwise_count(word, linenumber, sort = TRUE) word_cooccurences
# A tibble: 221,364 x 3 item1 item2 n
1 morty rick 461 2 rick morty 461 3 jerry rick 234 4 rick jerry 234 5 jerry morty 228 6 morty jerry 228 7 yeah rick 136 8 rick yeah 136 9 yeah morty 130 10 morty yeah 130 # ... with 221,354 more rows
This can be useful, for example, to plot a network of co-occuring words with the igraph and ggraph packages.
set.seed(1717) word_cooccurences %>% filter(n >= 25) %>% graph_from_data_frame() %>% ggraph(layout = "fr") + geom_edge_link(aes(edge_alpha = n, edge_width = n), edge_colour = "#a8a8a8") + geom_node_point(color = "darkslategray4", size = 8) + geom_node_text(aes(label = name), vjust = 2.2) + ggtitle(expression(paste("Word Network in Rick and Morty's ", italic("Season One")))) + theme_void()
It looks good! at least it contains the main characters and Rick’s swearing.