A Tidytext Analysis of 3 Chinese Classics

For a long time I’ve admired the tidytext package and its wonderful companion book Text Mining with R. After reading it I thought, “Why not undertake a project of Chinese text analysis?” I am deeply interested in Chinese philosophy but I decided to keep the analysis narrow by selecting just three works – The Analects, Zhuangzi, and the Mozi.

Following similar pace with Tidytext, I first download my data. Here I use my package ctextclassics and specifically, the function get_books(c(...)). But I want to point out the API limit is very low and I had to download my books between two different days. For information on ctextclassics, check out my previous post or type install_github("Jjohn987/ctextclassics")

library(tidyverse)
library(stringr)
library(ctextclassics)
library(tmcn)
library(tidytext)
library(topicmodels)
library(readr)

my_classics <- read_csv("~/Desktop/anything_data/content/post/my_classics.csv")

With any text analysis, tokenizing the text and filtering out stop words is fundamental. Tidytext segments English quite naturally, considering words are easily separated by spaces. However, I’m not so sure how it performs with Chinese characters.

There are specific segementers for Chinese text – one main tool is jiebaR, which is also included in the tmcn package.

However, when comparing the two methods, I noticed that JiebaR segments text in a way most suitable for modern Chinese (Mostly 2 character words). Since I’m dealing with classical Chinese here, Tidytext’s one character segmentaions are more preferable.

tidytext_segmented <- my_classics %>% 
  unnest_tokens(word, word)

For dealing with stopwords, JiebaR offers a useful stopword list, but obviously more should be added since we’re dealing with classical Chinese. Many of the words I added are amorphous grammar particles, but there’s other low value phrases amongst these works such as “子曰” (“The Master said”), “天下” (Tian Xia, a common but amorphous concept roughly meaning a country, realm, or the world), and more.

Let’s filter out those words and make 2 data frames – word frequencies for each book and each chapter.

stopwordsCN <- data.frame(word = c(tmcn::stopwordsCN(),
"子曰", "曰", "於", "則","吾", "子", "不", "無", "斯","與", "為", "必",
"使", "非","天下", "以為","上", "下", "人", "天", "不可", "謂", "是以",
"而不", "皆", "不亦", "乎", "之", "而", "者", "本", "與", "吾", "則",
"以", "其", "為", "不以", "不可", "也", "矣", "子", "由", "子曰", "曰",
"非其", "於", "不能", "如", "斯", "然", "君", "亦", "言", "聞", "今",
"君", "不知", "无"))

## Add a column that converts traditional Chinese to simplified Chinese
## Count words by book, then word frequency to account for different book lengths. 

counts_by_book <- tidytext_segmented %>%
  ungroup() %>%
  mutate(simplified = tmcn::toTrad(word, rev = TRUE), pinyin = tmcn::toPinyin(word)) %>%
  anti_join(stopwordsCN) %>%
  count(book, word, pinyin, simplified) %>%
  group_by(book) %>%
  mutate(word_freq = `n`/sum(`n`)) %>%
  arrange(-n) %>%
  ungroup()
## Warning: Column `word` joining character vector and factor, coercing into
## character vector

Now let’s do the familiar ritual of examining the top 10 words in each book (e.g, counts_by_book) and plot them.

book_top_words <- counts_by_book %>%
  ungroup() %>%
  group_by(book) %>%
  top_n(10) %>%
  ungroup()

##format the above dataframe for a pretty display with kable
formatted_words <- book_top_words %>%
  group_by(book) %>%
  transmute(word, simplified, n, word_freq, order = 1:n()) %>%
  arrange(book, -word_freq) %>%
  select(-order)

##Set format for kable 
options(knitr.table.format = "html") 

knitr::kable(formatted_words) %>%
  kableExtra::kable_styling(_size = 15, full_width = T) %>% kableExtra::row_spec(1:10, color = "white", background = "#232528") %>% kableExtra::row_spec(11:20, color = "white", background = "#6A656B") %>% kableExtra::row_spec(21:30, color = "white", background = "#454d4c") %>%
kableExtra::row_spec(0, bold = F, color = "black", background = "white")  %>% kableExtra::scroll_box(width = "100%", height = "350px")

So far so good – these results are very intuitive. Of course, plotting them can accomplish this in even greater detail. Let’s plot the top 10 words and their respective frequencies from each of these texts, in calligraphy inspired colors!

ink_colors <- rev(c("ivory", "#454d4c", "#6A656B", "#232528"))

ggplot(book_top_words, aes(x = reorder(word, word_freq), y = word_freq, fill = book)) +
  geom_col(show.legend = FALSE) + 
  geom_text(aes(label = pinyin), color = "white", position = position_stack(vjust = 0.5)) + 
  facet_wrap(~book, scales = "free", labeller = labeller(labels)) + 
  coord_flip() +
  scale_fill_manual(values = ink_colors) +
  theme_dark(base_family= "HiraKakuProN-W3") + 
  theme(axis.text.x = element_text(color = "#232528", angle = 90)) +
  theme(axis.text.y = element_text(color = "#232528", size = 15)) +
  theme(panel.background = element_rect(fill = "#87969B"), plot.background = element_rect(fill = "ivory"), panel.grid.major = element_blank(), panel.grid.minor = element_blank()) + 
  labs(x = NULL, y = NULL) +
  ggtitle("Word Frequencies") +
  theme(plot.title = element_text(size = 20, color = "#232528", hjust = 0.5))