Chinese leader’s speech at the significant political event is often a key moment for the public and the media to understand the country’s strategy and future development. This short paper aims to analyze and compare the speeches made by two Chinese Communist Party leaders at the Party Congress: Xi Jinping’s speech at the 19th Party Congress (2017) and Hu jintao’s the 17th party congress (2007).

First at all, we install the required libraries and load them into the system.

if (!require("wordcloud")) install.packages("wordcloud", repos="https://cran.cnr.berkeley.edu/")
if (!require("tm")) install.packages("tm", repos="https://cran.cnr.berkeley.edu/")

library("wordcloud")
library("tm")

Then, let’s obtain the copy of Xi Jinping’s speech at the 19th Party Congress.The file is in plain text format. The first five lines of his speech are displayed.

con <- url("https://raw.githubusercontent.com/jmsc-bc4j/JMSC6116_public/c3e6e6d6cdc555e78a83f1b81e6cc4c7b51d1377/Xi.txt") # Establish a connection via url
xi <- readLines(con)  # Read line by line from the connection to a string array xi
close(con) # Remember to close the connection after use
xi[1:5] # List the first five lines of Xi's speech
## [1] "Comrades,"                                                                                                                                                                                                                                                                               
## [2] ""                                                                                                                                                                                                                                                                                        
## [3] "On behalf of the 18th Central Committee of the Communist Party of China, I will now deliver a report to the 19th National Congress."                                                                                                                                                     
## [4] ""                                                                                                                                                                                                                                                                                        
## [5] "The 19th National Congress of the Communist Party of China is a meeting of great importance taking place during the decisive stage in building a moderately prosperous society in all respects and at a critical moment as socialism with Chinese characteristics has entered a new era."

Next, we get the copy of Hu jintao’s 17th Party Congress speech.

con <- url("https://raw.githubusercontent.com/jmsc-bc4j/JMSC6116_public/c3e6e6d6cdc555e78a83f1b81e6cc4c7b51d1377/Hu.txt")
hu <- readLines(con)
close(con)

Then, we define a R function, namely Preprocessing, to change all characters into lower case (for counting purpose), remove the punctuations and “stopwords”, and “clean” the text data into a format for next step. The first portion of the “cleaned” version of Xi’s speech are displayed.

Preprocessing <- function(doc){
  #create corpus
  doc.corpus <- Corpus(VectorSource(doc))
  #clean up
  doc.corpus <- tm_map(doc.corpus, function(x)chartr("ABCDEFGHIJKLMNOPQRSTUVWXYZ","abcdefghijklmnopqrstuvwxyz",x))### Convert to lower case
  doc.corpus <- tm_map(doc.corpus, removePunctuation)   ### remove punctuation
  doc.corpus <- tm_map(doc.corpus, function(x)removeWords(x,stopwords("english")))  #### remove stopwords
  return(doc.corpus)
}

xi <- paste(xi,collapse=" ") # Collapse into one single line
hu <- paste(hu,collapse=" ")
xi.p <- Preprocessing(xi) # Send the array xi to the function for data cleaning
hu.p <- Preprocessing(hu) # Output the results to xi.p and hu.p (class "Corpus")
substr(xi.p$content,1,200) # Inspect the first 200 words of Xi's "cleaned" speech
## [1] "comrades   behalf   18th central committee   communist party  china  will now deliver  report   19th national congress   19th national congress   communist party  china   meeting  great importance tak"

Ok. So we now compare Xi and Hu’s speeches by using histogram plot. To do this, a term document matrix, which stores the words used (by rows) and the two leaders (by columns), is created.

tdm <- TermDocumentMatrix(Corpus(VectorSource(c(xi.p$content,hu.p$content)))) # Create a term document matrix
tdm <- as.matrix(tdm) # convert it into a standard matrix
colnames(tdm) <- c("Xi's speech","Hu's speech") # Assign the names to the columns

Here you go. The top-5 high frequency term plot is presented.

Num_of_terms_shown <- 5
par(mfrow=c(1,2)) # 1x2 panel plot
barplot(tdm[order(tdm[,"Xi's speech"], decreasing=TRUE)[1:Num_of_terms_shown],"Xi's speech"],col="red", cex.names = 0.6, horiz=TRUE)
title("Xi Jinping's 19th Congress speech")
barplot(tdm[order(tdm[,"Hu's speech"], decreasing=TRUE)[1:Num_of_terms_shown],"Hu's speech"],col="blue", cex.names = 0.6, horiz=TRUE)
title("Hu jintao's 17th Congress speech")

Question: is term frequency a fair way for comparison? How can we make a better plot? Hints:

print(paste0("Total number of terms of Xi's speech (cleaned):",sum(tdm[,"Xi's speech"])))
## [1] "Total number of terms of Xi's speech (cleaned):14555"
print(paste0("Total number of terms of Hu's speech (cleaned):",sum(tdm[,"Hu's speech"])))
## [1] "Total number of terms of Hu's speech (cleaned):12315"

Next, we visualize the text frequency by using wordcloud function.

min.freq <- 50 # Minimium number of word frequency
par(mfrow=c(1,2)) # 1x2 panel plot
par(mar=c(1, 1, 1, 1)) # Set the plot margin
par(bg="black") # background color is black
par(col.main="white") # Title color is white
wordcloud(xi.p, scale=c(4,.5),min.freq=min.freq, max.words=Inf, random.order=F, colors=brewer.pal(8, "Accent"))   
title("Xi Jinping's 19th Congress speech")
wordcloud(hu.p, scale=c(4,.5),min.freq=min.freq, max.words=Inf, random.order=F, colors=brewer.pal(8, "Dark2"))   
title("Hu jintao's 17th Congress speech")

Finally, we generate a comparison wordcloud, which compares the relative frequency with which a term was used in the two documents. For example, Hu used the word “scientific” more frequenctly than did Xi and the word cloud thus prints the term “scientific” in the side of Hu. By the same token, you find “dream” is closer to Xi’s side. The plot shows the difference between the word usage of the leaders in their speeches and sheds light on the style of the leaders.

comparison.cloud(tdm,max.words=100,random.order=FALSE, colors=c("red","blue"))