Month: November 2016

A Short Introduction of Natsume Soseki

Natsume Soseki, born in 1867, the year before the Meiji Restoration, was a Japanese author whose works characterized the perplexity of Japanese during the era of rapid westernization. He loved Chinese literature, but studying English was a fashion at his time. Therefore, he became a scholar in English literature. The Japanese government sent him to study in England from 1901 to 1903, but this became his most unpleasant years. Soseki became mad in London, and started to question the idea of modernity. He was aware of the superficiality of Japanese westernization and aimless imitation of the west. In his works, he mainly focused on the pain and solitude that modernity brought to Japanese. Between 1905 and 1916, he wrote fifteen novels, including one unfinished. In 1907, Soseki rejected his professorship and started to work for Asahi newspaper, where most of his works were published. In 1916, he died of stomach ulcer.

Update on Historiographical Research

This is an update on last post. Natsume Soseki is commonly referred by his given name (or rather pen name), Soseki, so searching for “Soseki” in DfR of JSTOR collections is more accurate.

Text Mining of Soseki’s Novels

The central question that I am asking is what issues on modernity and tradition did Soseki write about and was Soseki more inclined to the traditional side or modern side.

The Japanese tokenizer MeCab with IPA dictionary takes in a txt file and produces a dataframe like the following graph. Term column is the word. Info1 is part of speech. Info2 is more information on part of speech.

I processed the txt files with the same code I did a month ago, taking out pronunciation guide and style annotations. Then, I used dataframes generated by MeCab to calculate the term frequency in percentage and tf-idf. For all the graphs, the novels names on the axes are in chronological order

First, I focused on term frequency of some keywords. In last post I mentioned that English scholars were interested in discussion wars in studies of Japanese literature. This interests are not unjustified, since most of Soseki’s works mentioned words related to military and war. Meiji Japan was also a time of military victories, like Sino-Japanese War (1894-95) and Russo-Japanese War (1904-05). These were directly related to the westernization of Japan.

Nevertheless, the word related to war was not that common compared to words related to love or death. In some of the works, Soseki showed his idea on love and solitude in modern world and how marriage in the new era should be different from old time. Maybe scholars should pay more attention to these issues.

For the other question, how did Soseki place himself in modernity and tradition. Although some famous critic, like Eto Jun thinks that Soseki stands on the side of tradition, especially in his last several works, more scholars argue that Soseki stands on side of modernity. Soseki was aware of the pain brought by westernization to Japanese, but he did not deny it. In some of his essays, he justified Japanese colonization of Manchuria and Korea by commenting that this was an inevitable result of a modernizing Japan.

The following figure presents frequency of four words, restoration (維新), enlightenment (開化), modernity (現代) and independent (独立), directly related to modernity and Meiji Restoration, in his novels. All of the novels used at least one of the four words.
The following graph is a comparison between the frequencies the word modernity (現代) and antiquity (古代).Soseki used a lot more modernity than antiquity.  I wanted to find the frequency of tradition (伝統) or national learning (国学), but it turned out that Soseki never used these words.

It might be the case that words like tradition or national learning emphasize superiority of Japanese culture, so Soseki avoided using them. His was disgusted by shallow nationalist movement of his classmates when he was young. By contrast the word Chinese study appeared several time in his novels.

Another way of looking at the question is to search for the words related to Chinese and English. For Soseki, Chinese is the more traditional culture and English is more modern, while the westernizing Japan is somewhere between modernity and tradition. Related words to Chinese include Qing Empire (清国), Japan-Qing (日清), China (中国), Chinese book (漢籍), Chinese land (漢土) and Chinese poetry (漢詩); related words to English include the UK (イギリス or 英国), English (英語 or 英文), Anglo-Japanese (英和) and English translation (英訳)

This graph is not as extreme as last one. Although words related to English is still more important, words related to Chinese appear a lot.

Tf-idf

The above equation is what I used for tf-idf. Both term frequency and document frequency are normalized.

Some of the novels are more interesting than other. Kokoro (The Hearts) is one of Soseki’s most beloved novel. The following graph is the top 5 words by tf-idf for each novel. Kokoro has the lowest tf-idf index, because it does not use many uncommon word. In other novels, characters has names, so the names have high tf-idf index. However, Soseki is reluctant to give characters names in Kokoro, so the most distinctive word is Zoshigaya (雑司ヶ谷), which is a place name in Tokyo.

Edwin McClellan, who introduced Soseki to western audience and translated two of Soseki’s novels, comments that Soseki wrote Kokoro as an “allegory of sorts”. Isolation and pain of Sensei, the protagonist in Volumn 3 of Kokoro, could be troubles to any Meiji intellectuals. The work is not only stylistically simple as noted by McClellan, but also lexically simple as shown in following graphs.

A violin graph of top 20 words by tf-idf also shows that Kokoro tends to use common word.

Code

### Text Processing code is in the post about Dazai Osamu

### Make frequency tables with RMeCab

Sys.setlocale(“LC_ALL”, “Japanese”) ### Windows users may want to use this to avoid encoding problems

library(RMeCab) ### Japanese Tokenizer

na.zzz <- RMeCabFreq(“D:/Google Drive/JPN_LIT/Natsume/zzz.txt”)
na.zzz.reduced <- na.zzz[which(na.zzz$Info1 != “記号”),] ###Take out punctuations

files.cle.dir<- list.files(“D:/Google Drive/JPN_LIT/Natsume/cleaned”)

for (n.i in 1:length(files.cle.dir)){
assign(paste0(“n.”, files.cle.dir[n.i]), RMeCabFreq(paste0(“D:/Google Drive/JPN_LIT/Natsume/cleaned/”,files.cle.dir[n.i]))[which(RMeCabFreq(paste0(“D:/Google Drive/JPN_LIT/Natsume/cleaned/”,files.cle.dir[n.i]))$Info1 != “記号”),])
}

###A sample code of combining frequency table of a novel to the frequency table of the corpus of fifteen novels.

z <- 1
y <- 1
while (z <= length(na.zzz.reduced$Term)){
if(n.bocchan.txt$Term[y] == na.zzz.reduced$Term[z] & n.bocchan.txt$Info1[y] == na.zzz.reduced$Info1[z] & n.bocchan.txt$Info2[y] == na.zzz.reduced$Info2[z]){
na.zzz.reduced$bocchan[z] <- n.bocchan.txt$Freq[y]
y <- y + 1
}
z <- z + 1
}

### na.zzz.reduced is the final frequency dataframe with every novel

### Tf-idf

n.TMD <- na.zzz.reduced[,4:19]
n.TMD$Dfreq <- apply(n.TMD, 1, function(x) length(which(x != 0)))
n.TMD$Dfnorm <- log(15/n.TMD$Dfreq +1)

n.TFIDF.df <- data.frame(t(apply(n.TMD[,2:16], 1, function(x) log(x)+1)))
n.TFIDF.df <- n.TFIDF.df*n.TMD$Dfnorm
n.TFIDF.df[n.TFIDF.df == -Inf] <- 0

Final.TFIDF.df <- cbind(na.zzz.reduced[,1:3],n.TFIDF.df)

##### Make another table of Final percentage

n.PERC.df<- data.frame(apply(n.TMD[,1:16], 2,function(x) x/sum(x)*100))
Final.PERC.df <- cbind(na.zzz.reduced[,1:3],n.PERC.df)

###Make a dataframe of top 20 and top 5 tfidf for each novel and change the name of wagahaiwa_nekodearu since it is too long
all.TFIDF20 <- data.frame(Term = NA,tfidf = NA, novel = NA)
for (col.i in 4:18){
all.TFIDF20 <- rbindlist(list(all.TFIDF20, cbind(Final.TFIDF.df[order(-Final.TFIDF.df[,col.i]),][1:20,][,c(1,col.i)],colnames(Final.TFIDF.df)[col.i])))
}
all.TFIDF20 <- all.TFIDF20[-1,]
all.TFIDF20$novel <- as.character(all.TFIDF20$novel)
all.TFIDF20[all.TFIDF20 == “wagahaiwa_nekodearu”] <- “wagahaiwa”

### Top5 tf-idf

all.TFIDF5 <- data.frame(Term = NA,tfidf = NA, novel = NA)
for (col.i in 4:18){
all.TFIDF5 <- rbindlist(list(all.TFIDF5, cbind(Final.TFIDF.df[order(-Final.TFIDF.df[,col.i]),][1:5,][,c(1,col.i)],as.character(colnames(Final.TFIDF.df)[col.i]))))
}
all.TFIDF5 <- all.TFIDF5[-1,]
all.TFIDF5$novel <- as.character(all.TFIDF5$novel)

### Violin Graph

ggplot(all.TFIDF20, aes(x = novel, y = tfidf))+
geom_point(size = 2) +
scale_x_discrete(limits=c(“wagahaiwa”,”bocchan”,”kusamakura”,”nihyakutoka”,”nowaki”,”gubijinso”,”kofu”,”sanshiro”,”sorekara”,”mon”,”higansugimade”,”kojin”,”kokoro”,”michikusa”,”meian”)) +
geom_violin()

### Top 5 tf-idf with term shown

library(ggrepel) ### An extension to ggplot2 to avoid overlap of terms in graph

ggplot(all.TFIDF5, aes(x = novel, y = tfidf))+
geom_point(size = 2) +
scale_x_discrete(limits=c(“wagahaiwa”,”bocchan”,”kusamakura”,”nihyakutoka”,”nowaki”,”gubijinso”,”kofu”,”sanshiro”,”sorekara”,”mon”,”higansugimade”,”kojin”,”kokoro”,”michikusa”,”meian”)) +
geom_text_repel(aes(label=Term), size =4, segment.color = ‘grey60’,nudge_x =0.05)

### A sample graph of how I made the table for words related to Chinese and English. All other frequency graphs are similar (I admit that I used Mspaint to change some of the legend, because it is more convenient)
chn.all.PERC.df <- Final.PERC.df[which(Final.PERC.df$Term == “清国” |
Final.PERC.df$Term == “中国”|
Final.PERC.df$Term == “漢学”|
Final.PERC.df$Term ==”漢語”|
Final.PERC.df$Term ==”漢詩”|
Final.PERC.df$Term ==”漢籍”|
Final.PERC.df$Term ==”漢土”|
Final.PERC.df$Term ==”漢”|
Final.PERC.df$Term ==”漢人”),] ### I choose the terms based on search for three words, “清”, “漢” and “中国”.

eng.all.PERC.df <- Final.PERC.df[which(Final.PERC.df$Term == “イギリス” |
Final.PERC.df$Term == “英国”|
Final.PERC.df$Term == “英訳”|
Final.PERC.df$Term ==”英語”|
Final.PERC.df$Term ==”英文”|
Final.PERC.df$Term ==”英和”),]### The choice of vocabularies is similarly determined by the research intention
chn.eng.df <- data.frame(apply(chn.all.PERC.df[,5:19], 2, function(x) sum(x)))
chn.eng.df <- cbind(chn.eng.df, data.frame(apply(eng.all.PERC.df[,5:19], 2, function(x) sum(x))))

colnames(chn.eng.df) <- c(“CHINESE”, “ENGLISH”)

chn.eng.df$novel <- row.names(chn.eng.df)

ggplot(chn.eng.df, aes(x = novel))+
geom_point(aes(y=CHINESE, color = “CHINESE”), shape = 8, size =3) +
geom_point(aes(y=ENGLISH, color = “ENGLISH”), shape = 1, size =3) +
scale_x_discrete(limits=rev(c(“wagahaiwa_nekodearu”,”bocchan”,”kusamakura”,”nihyakutoka”,”nowaki”,”gubijinso”,”kofu”,”sanshiro”,”sorekara”,”mon”,”higansugimade”,”kojin”,”kokoro”,”michikusa”,”meian”))) +
labs(color=”Keywords”)+
ylab(“Freq”) +
coord_flip()

Frequency of Imperial Titles in the Theodosian Code

Frequency of Those Words (not just as titles) in the Theodosian Code

Frequency of Clementia as Imperial Title, by Reign

Code

#THEODOSIAN CODE

CTh.scan <- scan(“~/Education/Emory/Coursework/Digital Humanities Methods/Project/Theodosian Code Raw Text.txt”,
what=”character”, sep=”\n”)
CTh.df <- data.frame(CTh.scan, stringsAsFactors=FALSE)
CTh.df <- str_replace_all(string = CTh.df$CTh.scan, pattern = “[:punct:]”, replacement = “”)
CTh.df <- data.frame(CTh.df, stringsAsFactors = FALSE)
CTh.lines <- tolower(CTh.df[,1])
book.headings <- grep(“book”, CTh.lines)
start.lines <- book.headings + 1
end.lines <- book.headings[2:length(book.headings)] – 1
end.lines <- c(end.lines, length(CTh.lines))
CTh.df <- data.frame(“start” = start.lines, “end”=end.lines, “text”=NA)
i <- 1
for (i in 1:length(CTh.df$end))
{CTh.df$text[i] <- paste(CTh.lines[CTh.df$start[i]:CTh.df$end[i]], collapse = ” “)}

CTh.df$Book <- seq.int(nrow(CTh.df))

#String Extracts of Imperial Titles

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}aeternita.{0,80}|.{0,80}aeternita.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}aeternita.{0,80}|.{0,80}aeternita.{0,80}mea.{0,80}”) #AETERNITAS

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}clementia.{0,80}|.{0,80}clementia.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}clementia.{0,80}|.{0,80}clementia.{0,80}mea.{0,80}”) #CLEMENTIA

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}lenita.{0,80}|.{0,80}lenita.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}lenita.{0,80}|.{0,80}lenita.{0,80}mea.{0,80}”) #LENITAS

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}lenitud.{0,80}|.{0,80}lenitud.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}lenitud.{0,80}|.{0,80}lenitud.{0,80}mea.{0,80}”) #LENITUDO

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}maiesta.{0,80}|.{0,80}maiesta.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}maiesta.{0,80}|.{0,80}maiesta.{0,80}mea.{0,80}”) #MAIESTAS

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}mansuetud.{0,80}|.{0,80}mansuetud.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}mansuetud.{0,80}|.{0,80}mansuetud.{0,80}mea.{0,80}”) #MANSUETUDO

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}moderatio.{0,80}|.{0,80}moderatio.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}moderatio.{0,80}|.{0,80}moderatio.{0,80}mea.{0,80}”) #MODERATIO

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostrum.{0,80}numen.{0,80}|.{0,80}numen.{0,80}nostrum.{0,80}|.{0,80}nostr.{0,80}numin.{0,80}|.{0,80}numin.{0,80}nostr.{0,80}|.{0,80}meum.{0,80}numen.{0,80}|.{0,80}numen.{0,80}meum.{0,80}|.{0,80}me.{0,80}numin.{0,80}|.{0,80}numin.{0,80}me.{0,80}”) #NUMEN

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}perennita.{0,80}|.{0,80}perennita.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}perennita.{0,80}|.{0,80}perennita.{0,80}mea.{0,80}”) #PERENNITAS

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}pieta.{0,80}|.{0,80}pieta.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}pieta.{0,80}|.{0,80}pieta.{0,80}mea.{0,80}”) #PIETAS

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}scientia.{0,80}|.{0,80}scientia.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}scientia.{0,80}|.{0,80}scientia.{0,80}mea.{0,80}”) #SCIENTIA

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}serenita.{0,80}|.{0,80}serenita.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}serenita.{0,80}|.{0,80}serenita.{0,80}mea.{0,80}”) #SERENITAS

str_extract_all(string = CTh.df$text, pattern = “.{0,80}nostra.{0,80}tranquillita.{0,80}|.{0,80}tranquillita.{0,80}nostra.{0,80}|.{0,80}mea.{0,80}tranquillita.{0,80}|.{0,80}tranquillita.{0,80}mea.{0,80}”) #TRANQUILLITAS

#Imperial Title Sums

aeternitas <- 2
clementia <- 93
lenitas <- 2
lenitudo <- 2
maiestas <- 12
mansuetudo <- 59
moderatio <- 2
numen <- 27
perennitas <- 12
pietas <- 9
scientia <- 32
serenitas <- 57
tranquillitas <- 10

#Imperial Title Sum Graph

Frequency <- c(aeternitas, clementia, lenitas, lenitudo, maiestas, mansuetudo, moderatio, numen, perennitas, pietas, scientia, serenitas, tranquillitas)
Title <- c(“Aeternitas”, “Clementia”, “Lenitas”, “Lenitudo”, “Maiestas”, “Mansuetudo”, “Moderatio”, “Numen”, “Perennitas”, “Pietas”, “Scientia”, “Serenitas”, “Tranquillitas”)
sum.df <- cbind.data.frame(Title, Frequency)
sum.df$Title <- factor(sum.df$Title, levels = sum.df$Title[order(sum.df$Frequency)]) #Reorders dataframe based on Frequency

ggplot(data=sum.df, aes(x=Title, Frequency), y=Frequency) + geom_bar(stat = “identity”) + coord_flip() #Word Total Graph

#Non-Title Frequencies

aeternitas <- sum(str_count(CTh.df$text, “aeternita”), na.rm = TRUE)
clementia <- sum(str_count(CTh.df$text, “clementia”), na.rm = TRUE)
lenitas <- sum(str_count(CTh.df$text, “lenita”), na.rm = TRUE)
lenitudo <- sum(str_count(CTh.df$text, “lenitud”), na.rm = TRUE)
maiestas <- sum(str_count(CTh.df$text, “maiesta”), na.rm = TRUE)
mansuetudo <- sum(str_count(CTh.df$text, “mansuetud”), na.rm = TRUE)
moderatio <- sum(str_count(CTh.df$text, “moderatio”), na.rm = TRUE)
numen <- sum(str_count(CTh.df$text, “numen|numin”), na.rm = TRUE)
perennitas <- sum(str_count(CTh.df$text, “perennita”), na.rm = TRUE)
pietas <- sum(str_count(CTh.df$text, “pieta”), na.rm = TRUE)
scientia <- sum(str_count(CTh.df$text, “scientia”), na.rm = TRUE)
serenitas <- sum(str_count(CTh.df$text, “serenita”), na.rm = TRUE)
tranquillitas <- sum(str_count(CTh.df$text, “tranquillita”), na.rm = TRUE)

Frequency <- c(aeternitas, clementia, lenitas, lenitudo, maiestas, mansuetudo, moderatio, numen, perennitas, pietas, scientia, serenitas, tranquillitas)
Title <- c(“Aeternitas”, “Clementia”, “Lenitas”, “Lenitudo”, “Maiestas”, “Mansuetudo”, “Moderatio”, “Numen”, “Perennitas”, “Pietas”, “Scientia”, “Serenitas”, “Tranquillitas”)
sum.df <- cbind.data.frame(Title, Frequency)
sum.df$Title <- factor(sum.df$Title, levels = sum.df$Title[order(sum.df$Frequency)]) #Reorders dataframe based on Frequency

ggplot(data=sum.df, aes(x=Title, Frequency), y=Frequency) + geom_bar(stat = “identity”) + coord_flip() #Word Total Graph

#Title Frequency By Reign

constantine <- 9
constantius <- 5
valentinian1 <- 5
valens <- 3
gratian <- 1
valentinian2 <- 1
theodosius1 <- 7
honorius <- 21
arcadius <- 9
theodosius2 <- 23

Frequency <- c(constantine, constantius, valentinian1, valens, gratian, valentinian2, theodosius1, honorius, arcadius, theodosius2)
Title <- c(“Constantine (306-337)”, “Constantius (337-361)”, “Valentinian I (364-375)”, “Valens (364-378)”, “Gratian (375-383)”, “Valentinian II (375-392)”, “Theodosius I (379-395)”, “Honorius (395-423)”, “Arcadius (395-408)”, “Theodosius II (408-450)”)
sum.df <- cbind.data.frame(Title, Frequency)
sum.df$Title <- factor(sum.df$Title, levels = sum.df$Title[order(sum.df$Frequency)]) #Reorders dataframe based on Frequency

ggplot(data=sum.df, aes(x=Title, Frequency), y=Frequency) + geom_bar(stat = “identity”) + labs(x = “Emperor”) + coord_flip() #Word Total Graph

Rubicon and Demographics Correlation Matrix

Terms that Correlate Strongly With “Rubicon”

A ‘Mecca of Patriotism’: The Commemorative Monuments of the Guilford Battle Ground Park and Shifting Views toward Historic Preservation

Nathanael Greene Monument. Photo courtesy of the National Park Service.

Guilford Courthouse National Military Park is located in north-central North Carolina about six miles northwest of downtown Greensboro. The park encompasses about 220 acres, which protect the core of the largest, most hotly contested battle of the American Revolution’s climactic Southern Campaign. In 1887, under the direction of Judge David Schenck, the Guilford Battle Ground Company (GBGC) was chartered for the purpose of preserving and adorning the American Revolution battlefield at Guilford Courthouse in North Carolina. Motivated foremost by patriotism, the GBGC erected approximately 30 monuments and memorials between 1888 and 1917 at the Guilford Battle Ground Park, of which seven marked grave sites.  The history of commemoration at Guilford reflects the developing national commemorative movement that emerged in America in the late 1800s and continued through the early 1900s.

While the GBGC erected the majority of monuments at the battlefield, the War Department continued the tradition from 1917 through 1933 by adding five monuments at the newly established Guilford Courthouse National Military Park (GUCO NMP). Since the National Park Service (NPS) began managing GUCO NMP in 1933, it has removed six monuments from the battlefield and relocated others. In 2016, GUCO NMP gained a new monument sponsored by the reinstated Guilford Battle Ground Company with assistance from several British Regimental Associations to recognize the British Regiments associated with the battle. This monument was the first erected at Guilford in nearly 84 years, as well as the only one associated with the NPS’s management period.

Although the GBGC, the War Department, and the NPS have shared the same underlying goal of preserving the historic Guilford battlefield, each entity has taken its own approach to achieve this end. In my paper, I will examine how the creation and removal of monuments throughout the various periods at Guilford correlate with shifting cultural attitudes and ideas toward commemoration and historic preservation. I will use a combination of qualitative and quantitative methods to identify patterns in the monuments at Guilford—ranging from the individuals and groups who sponsored the monuments to the subjects they honored, their materials, artistic styles, and distinct placement in the landscape.

Above, a positive and negative bar graph showing trends in the erection and removal of monuments at Guilford Courthouse National Military Park across the decades in relation to its different periods of management under the Guilford Battle Ground Company, the War Department, and the National Park Service.

Above, image from the Google Books Ngram Viewer showing how the words “monument” and “patriotism” have occurred throughout a corpus of American English books from 1880-2000. The peak and decline in use of this word in writing generally follows the overall trend of the erection and removal of monuments at Guilford and other sites across the country.

Above, line graphs shows how the development of the Guilford Battle Ground Park paralleled the growth of the city of Greensboro.

In 1890, Schenck wrote:

“Now that Greensboro has the certain prospect of becoming a large city and extending northward towards the Battle Ground, it is easy to foresee that so interesting and beautiful a place as this, abounding in shade, and supplied with abundance of the purest water, must in the near future, become the park of the city, where its citizens can go for rest and recreation; and that summer cottages will be built up around it where the families of the city can escape the heat and dust and enjoy the fresh air of a delightful country resort.”[1]

[1] David Schenck, “To the Stockholders of the Guilford Battle Ground Company, Greensboro, NC, March 15, 1890,” in David Schenck Papers, 1849-1917, Folder 16: Volume 15: 1887-1900: Scan 36 (Southern Historical Collection, The Wilson Library, University of North Carolina at Chapel Hill).

Above, a map showing the concentrations of monuments commemorating the American Revolution erected in North Carolina in the 1890s. In this decade, the state gained 10 new monuments commemorating the American Revolution of which nine were erected at the Guilford Battle Ground Park.

*During the first decade of the 20th century, the state did not gain any monuments commemorating the American Revolution. While Guilford (and other sites) did gain monuments during this period, the subjects they commemorated bore other associations. For example, during this decade the Guilford Battle Ground Company erected monuments to commemorate Judge David Schenck, the company’s first president, and to Clio, the Muse of History, among others.

Above, a map showing the concentrations of monuments commemorating the American Revolution erected in North Carolina in the 1910s. In the decade, commemoration expanded to other areas across the state, such as near the cities of Raleigh and Fayetteville, and Wilmington. Of the 17 monuments erected across the state during this period to commemorate the American Revolution, over half were at the Guilford Battle Ground Park. Note that the map also shows one American Revolution monument erected near the border in Blacksburg, South Carolina where the battle of Kings Mountain occurred.

Above, map showing the concentrations of monuments commemorating the American Revolution erected in North Carolina in the 1920s.

Above, map showing the concentrations of monuments commemorating the American Revolution erected in North Carolina in the 1930s. At this point, the numbers across the state are dwindling.

Above, map showing the concentrations of monuments commemorating the American Revolution erected in North Carolina in the 1940s. There were no other Revolutionary War monuments erected in the state until July 2016 when Guilford gained its new Crown Forces monument.

The following bar graphs show patterns in the materials, styles, and subjects of the monuments.

The vast majority of the monuments at Guilford were carved from granite due to its abundance in the area and accessibility from the Mount Airy Granite Quarry. While several of the monuments incorporated cast bronze sculptures, many others contained bronze tablets with quotations. In the latter half of the nineteenth century, the popularity of bronze eclipsed marble as a medium for sculpture due to the development of specialized foundries and the proliferation of trained labor and equipment. Much of the bronze work at Guilford can be traced to two foundries: the Bureau Brothers of Philadelphia, Pennsylvania, and W. H. Mullins, Manufacturer of Architectural Sheet Metal Work and Statuary, of Salem, Ohio.

In addition to seeing Guilford as a “park of the city,” Schenck also saw the site as the state’s common burial ground for the American Revolution. Accordingly, the vast majority of monuments at Guilford honored “successful heroes and statesmen.” There were a few, however, commemorating historical female figures, as well as others that commemorated other events, such as the Battle of Kings Mountain. Thus, it is not surprising that a large percentage of monuments at Guilford do not have a direct association with the Battle of Guilford Courthouse.

The GBGC-era reveals how historic preservation was characterized by attempts to keep certain memories alive through the creation of monuments  and memorials. During the NPS-era, cultural attitudes shifted away from the production of monuments as historic preservation focused more on “authenticity” and “integrity” of the site’s Revolutionary War period. More recently, the NPS has begun to recognize the significance of the site’s commemorative period. Today, Guilford presents a key preservation challenge for its managers, who must determine how to balance the site’s past with its present as its significance continues to fluctuate over time.

R Codes

#To create a bar graph showing the addition and removal of Monuments at Guilford across the decades
library(ggplot2)
dat <- read.table(text = “ Variable Decade Monuments
1 Added 1880s 3
2 Removed 1880s 0
3 Added 1890s 10
4 Removed 1890s 0
5 Added 1900s 11
6 Removed 1900s 0
7 Added 1910s 4
8 Removed 1910s 0
9 Added 1920s 3
10 Removed 1920s 0
11 Added 1930s 3
12 Removed 1930s -4
13 Added 1940s 0
14 Removed 1940s 0
15 Added 1950s 0
16 Removed 1950s 0
17 Added 1960s 0
18 Removed 1960s -1
19 Added 1970s 0
20 Removed 1970s -1
21 Added 1980s 0
22 Removed 1980s 0
23 Added 1990s 0
24 Removed 1990s 0
25 Added 2000s 0
26 Removed 2000s 0
27 Added 2010s 1
28 Removed 2010s 0”,header = TRUE,sep = “”,row.names = 1)
dat1 <- subset(dat,Monuments >= 0)
dat2 <- subset(dat,Monuments < 0)

ggplot() +
geom_bar(data = dat1, aes(x=Decade, y=Monuments, fill=Variable),stat = “identity”) +
geom_bar(data = dat2, aes(x=Decade, y=Monuments, fill=Variable),stat = “identity”) +
geom_bar(stat = “identity”, color = “black”) +
scale_fill_manual(values =c(“#66cc99”, “#ff6666”)) +
guides(fill = guide_legend(override.aes = list(colour = NULL))) +
guides(colour = FALSE) +
ggtitle(“Monuments at Guilford Courthouse National Military Park”) +labs(x=”Decade”, y=”Number”) +
geom_hline(yintercept=0)

#To create a map of North Carolina showing the distribution of American Revolution monuments in the 1890s **I applied the same code to create other maps, but with different coordinates and sizes for the points. I had difficulty creating annotations in R so I used Adobe Illustrator.

library(ggmap)
myLocation <- c(-84.917575, 33.954619, -75.002153, 36.679869) #creates a map of North Carolina based on bottom left and top right coordinates
myMap <- get_map(location=myLocation,
source=”google”, maptype = “terrain”, crop=FALSE, zoom = 7) #defines the source and type of the map, as well as its zoom
ggmap(myMap)+
geom_point(aes(x = -80.842286, y = 35.222339), colour = “red”, alpha = .5, size = 4)+
geom_point(aes(x = -79.798653, y = 36.046642), colour = “red”, alpha = .5, size = 6) #defines the points on the map and their sizes

#To create a line graph showing the number of monuments erected at Guilford per decade in the late 1800s
monuments <- c(0, 3, 10, 11) #creates the point values for the line
g_range <- range(0, monuments) #creates the range for the y-axis
plot(monuments, type=”o”, col=”green”, ylim=g_range,
axes=FALSE, ann=FALSE) #plots the green line
axis(1, at=1:4, lab=c(“1870s”,”1880s”, “1890s”, “1900s”)) #adjusts the labels on the x-axis
axis(2, las=1, at=1*0:g_range[2]) #adjusts the tick marks on the y-axis
title(main=”Guilford Monuments Erected in the Late 19th Century”, col.main=”black”, font.main=4) #adds a main title in black and italics
box() #adds a box around the graph
title(xlab=”Decade”, col.lab=”black”) #adds a black title to the x-axis
title(ylab=”No. of Monuments Erected”, col.lab=”black”) #adds a black title to the y-axis

#To create a line graph showing Greensboro’s increase in population
population <- c(1497, 2105, 3317, 10035) #creates the point values for the population correlating with each decade plotted
g_range <- range(0, population) #creates the range for the y-axis
plot(population, type=”o”, col=”blue”, ylim=g_range,
axes=FALSE, ann=FALSE) #plots the blue line
axis(1, at=1:4, lab=c(“1870”,”1880”, “1890”, “1900”)) #labels the decades on the x axis
axis(2, las=1, at=1000*0:g_range[2]) #adjusts the tick marks on the y-axis
title(main=”Late Nineteenth-Century Population Growth of Greensboro”, col.main=”black”, font.main=4) #adds a main title in black and italics
box() #adds a box around the graph
title(xlab=”Decade”, col.lab=”black”) #adds a black title to the x-axis
title(ylab=”Population”, col.lab=”black”) #adds a black title to the y-axis

#To create a pie chart showing percentages of monuments either directly or not associated with the battle
x <- c(23, 14) #creates the values
labels <- c(“Directly Associated”, “Not Associated”) #creates the labels for the values
pie(x, labels, main = “Percentage of Guilford Monuments \n Associated with the Battle”, col = grey.colors(length(x))) #creates the title split on two lines and fills the chart with a grey scheme

#To create a bar graph ranking the subjects of monuments
library(ggplot2)
dat <- read.table(text = “Subject Number
1 Military-Figure-Male 26
2 Historic-Figure-Female 3
3 Civic-Figure-Male 2
4 Political-Figure-Male 3
5 Historic-Event 2”,header = TRUE,sep = “”,row.names = 1)
ggplot(dat, aes(x=reorder(Subject, -Number), y=Number)) +
geom_bar(stat=”identity”) +
ggtitle(“Subjects of Monuments \n at Guilford Courthouse National Military Park”)+
xlab(label = “Subjects”)+
ylab(label = “Number of Monuments”)+
scale_y_continuous(breaks = c(0,5,10,15,20,25, 30))+
coord_flip()

#To Create a bar graph ranking the styles of monuments
library(ggplot2)
dat <- read.table(text = “Style Number
1 Statue 7
2 Slab 1
3 Boulder 2
4 Tombstone 4
5 Stepped-Pyramid 1
6 Obelisk 5
7 Upright-Block 8
8 Slanted-Block 2
9 Diamond-shaped-Block 1
10 Prism-shaped-Block 1
11 Column-Shaft 3
12 Arch 2”,header = TRUE,sep = “”,row.names = 1)
ggplot(dat, aes(x=reorder(Style, -Number), y=Number)) +
geom_bar(stat=”identity”) +
ggtitle(“Styles of Monuments \n at Guilford Courthouse National Military Park”)+
xlab(label = “Style”)+
ylab(label = “Number of Monuments”)+
coord_flip()

#To Create a bar graph ranking the materials used for monuments
library(ggplot2)
dat <- read.table(text = “Material Number
1 Granite 26
2 Bronze 20
3 Marble 5
4 Copper 1
5 Composition_Metal 1”,header = TRUE,sep = “”,row.names = 1)
ggplot(dat, aes(x=reorder(Material, -Number), y=Number)) +
geom_bar(stat=”identity”) +
ggtitle(“Materials Used for Monuments \n at Guilford Courthouse National Military Park”)+
xlab(label = “Material”)+
ylab(label = “Number of Monuments”)+
scale_y_continuous(breaks = c(0,5,10,15,20,25, 30))+
coord_flip()

Text Mining “Rubicon”

Rubicon was the state’s first and largest drug treatment center and offered a plethora of treatment options including methadone maintenance.  After gathering all the mentions of “Rubicon” available in four newspaper across the state, the year 1973 seems to bear relevance in the rehabilitation sector as well.

Va drugs in motion

Articles about “Rubicon” in Newspaper CorpusWords that indicate ties with the justice system 1971-1974

Sentiment Analysis of articles about Rubicon in three Virginia newspapers

Word cluster of mentions of “police” within the Rubicon corpus. The city of Petersburg is heavily represented.

Sorry for the delay on my blog post! I’ve finally managed to figure out the coding to search for all inflections of the various nostra/mea epithets in Latin documents. I was having trouble using .*? to account for varying numbers of characters between nostra/mea and its accompanying noun (e.g. nostra clementia), as R was, despite the “?”, still being far too greedy. str_locate_all showed that it was pairing nostra‘s and titles that were thousands of characters apart!

My solution has been to ask R to search for combinations of nostra/mea and the accompanying noun with anywhere from 0 to 80 characters inbetween. Furthermore, I’ve simplified my code by only searching for the parts of these words that don’t inflect. So, for example, I wrote:

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}clementia.{0,80}|clementia.{0,80}nostra.{0,80}|mea.{0,80}clementia.{0,80}|clementia.{0,80}mea.{0,80}”) #CLEMENTIA

This accounts for all inflections; it turns up nostra/mea clementia, nostrae/meae clementiae, and nostram/meam clementiam. I did this for all of the imperial epithets that I have identified within the Theodosian Code. I then used those results to locate and read each instance in the Latin text, both so as to confirm their use as imperial epithets within their respective contexts, and so as to record their exact location within the Code. It’s been time consuming, but very rewarding. I now have complete and accurate results for their frequency within the Code:

Now that I have an effective formula down, I will run through the rest of my documents this week: the main ones are the Code of Justinian, Symmachus’ Relationes to the emperors, and a series of Latin Panegyrics. I hope to have a few of these done before class on Thursday; I’ll update this post with those results.

My code

#THEODOSIAN CODE

CTh.scan <- scan(“~/Education/Emory/Coursework/Digital Humanities Methods/Project/Theodosian Code Raw Text.txt”,
what=”character”, sep=”\n”)
CTh.df <- data.frame(CTh.scan, stringsAsFactors=FALSE)
CTh.df <- str_replace_all(string = CTh.df$CTh.scan, pattern = “[:punct:]”, replacement = “”)
CTh.df <- data.frame(CTh.df, stringsAsFactors = FALSE)
CTh.lines <- tolower(CTh.df[,1])
book.headings <- grep(“book”, CTh.lines)
start.lines <- book.headings + 1
end.lines <- book.headings[2:length(book.headings)] – 1
end.lines <- c(end.lines, length(CTh.lines))
CTh.df <- data.frame(“start” = start.lines, “end”=end.lines, “text”=NA)
i <- 1
for (i in 1:length(CTh.df$end))
{CTh.df$text[i] <- paste(CTh.lines[CTh.df$start[i]:CTh.df$end[i]], collapse = ” “)}

CTh.df$Book <- seq.int(nrow(CTh.df))

#String Extracts of Imperial Titles

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}aeternita.{0,80}|aeternita.{0,80}nostra.{0,80}|mea.{0,80}aeternita.{0,80}|aeternita.{0,80}mea.{0,80}”) #AETERNITAS

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}clementia.{0,80}|clementia.{0,80}nostra.{0,80}|mea.{0,80}clementia.{0,80}|clementia.{0,80}mea.{0,80}”) #CLEMENTIA

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}lenita.{0,80}|lenita.{0,80}nostra.{0,80}|mea.{0,80}lenita.{0,80}|lenita.{0,80}mea.{0,80}”) #LENITAS

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}lenitud.{0,80}|lenitud.{0,80}nostra.{0,80}|mea.{0,80}lenitud.{0,80}|lenitud.{0,80}mea.{0,80}”) #LENITUDO

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}maiesta.{0,80}|maiesta.{0,80}nostra.{0,80}|mea.{0,80}maiesta.{0,80}|maiesta.{0,80}mea.{0,80}”) #MAIESTAS

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}mansuetud.{0,80}|mansuetud.{0,80}nostra.{0,80}|mea.{0,80}mansuetud.{0,80}|mansuetud.{0,80}mea.{0,80}”) #MANSUETUDO

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}moderatio.{0,80}|moderatio.{0,80}nostra.{0,80}|mea.{0,80}moderatio.{0,80}|moderatio.{0,80}mea.{0,80}”) #MODERATIO

str_extract_all(string = CTh.df$text, pattern = “nostrum.{0,80}numen.{0,80}|numen.{0,80}nostrum.{0,80}|nostr.{0,80}numin.{0,80}|numin.{0,80}nostr.{0,80}|meum.{0,80}numen.{0,80}|numen.{0,80}meum.{0,80}|me.{0,80}numin.{0,80}|numin.{0,80}me.{0,80}”) #NUMEN

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}perennita.{0,80}|perennita.{0,80}nostra.{0,80}|mea.{0,80}perennita.{0,80}|perennita.{0,80}mea.{0,80}”) #PERENNITAS

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}pieta.{0,80}|pieta.{0,80}nostra.{0,80}|mea.{0,80}pieta.{0,80}|pieta.{0,80}mea.{0,80}”) #PIETAS

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}scientia.{0,80}|scientia.{0,80}nostra.{0,80}|mea.{0,80}scientia.{0,80}|scientia.{0,80}mea.{0,80}”) #SCIENTIA

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}serenita.{0,80}|serenita.{0,80}nostra.{0,80}|mea.{0,80}serenita.{0,80}|serenita.{0,80}mea.{0,80}”) #SERENITAS

str_extract_all(string = CTh.df$text, pattern = “nostra.{0,80}tranquillita.{0,80}|tranquillita.{0,80}nostra.{0,80}|mea.{0,80}tranquillita.{0,80}|tranquillita.{0,80}mea.{0,80}”) #TRANQUILLITAS

#Imperial Title Sums

aeternitas <- 2
clementia <- 93
lenitas <- 2
lenitudo <- 2
maiestas <- 12
mansuetudo <- 59
moderatio <- 2
numen <- 27
perennitas <- 12
pietas <- 9
scientia <- 32
serenitas <- 57
tranquillitas <- 10

#Imperial Title Sum Graph

Frequency <- c(clementia, mansuetudo, serenitas, scientia, numen, maiestas, tranquillitas, pietas, aeternitas, lenitas, lenitudo, moderatio)
Title <- c(“Clementia”, “Mansuetudo”, “Serenitas”, “Scientia”, “Numen”, “Maiestas”, “Tranquillitas”, “Pietas”, “Aeternitas”, “Lenitas”, “Lenitudo”, “Moderatio”)
sum.df <- cbind.data.frame(Title, Frequency)
sum.df$Title <- factor(sum.df$Title, levels = sum.df$Title[order(sum.df$Frequency)]) #Reorders dataframe based on Frequency

ggplot(data=sum.df, aes(x=Title, Frequency), y=Frequency) + geom_bar(stat = “identity”) + coord_flip() #Word Total Graph

Index of Imperial Epithets in the Theodosian Code

Nostra Aeternitas

10.22.3

Mea Aeternitas

12.1.160

Nostra Clementia

1.1.5
1.7.4
1.14.1
2.6.1
2.8.20
2.23.1
5.1.2
5.2.1
5.15.21
5.16.31
6.2.26
6.4.18
6.4.33
6.23.4
6.30.4
6.35.14
7.1.16
7.1.17
7.4.21
7.4.25
7.6.5
7.13.13
7.21.4
8.5.1
8.5.5
8.5.30
8.5.44
8.5.50
8.5.54
8.5.56
8.5.57
8.10.3
9.16.12
9.17.2
9.21.6
9.34.7
9.40.16
9.40.16
9.41.1
9.45.4
10.1.16
10.10.26
10.10.32
10.10.34
10.14.1
10.15.2
11.7.15
11.16.7
11.16.8
11.20.4
11.28.3
11.28.14
11.30.13
11.30.54
11.30.57
11.30.61
11.36.24
12.1.14
12.1.14
12.1.15
12.1.146
12.1.169
12.1.184
12.6.30
12.10.1
12.12.4
12.12.14
13.1.20
13.3.17
14.10.3
14.15.5
14.17.5
14.17.14
15.1.44
15.1.49
15.3.4
15.6.1
16.1.2
16.2.42
16.3.2
16.5.46
16.5.49
16.5.54
16.5.54
16.5.60
16.5.63
16.8.17
16.11.2

Mea Clementia

1.8.2
1.8.3
6.26.17
7.16.2
11.20.5

Nostra Lenitas

1.22.2
10.8.3

Nostra Lenitudo

8.12.6
15.1.5

Nostra Maiestas

6.21.1
6.27.17
6.27.17
8.4.26
8.5.39
11.29.1
11.30.66
11.30.68
13.3.18
14.3.18
15.1.47
16.10.20

Nostra Mansuetudo

1.2.8
1.5.9
1.10.1
1.15.8
1.28.1
3.9.1
4.14.1
6.2.19
6.22.8
6.23.4
6.30.18
6.30.20
7.13.9
8.5.12
8.5.22
8.5.54
8.5.58
8.8.2
8.10.2
9.16.10
9.30.2
10.7.2
10.7.2
10.9.2
10.9.3
10.10.20
10.16.2
11.7.21
11.12.4
11.16.11
11.16.14
11.28.3
11.28.5
11.30.32
11.30.41
11.30.41
12.6.5
12.6.12
12.6.28
12.12.5
12.12.10
12.12.10
12.19.3
13.3.4
13.5.38
13.6.5
14.1.2
14.4.3
14.9.1
15.3.1
15.5.5
15.7.4
15.7.6
15.7.9
16.2.12
16.5.7
16.5.38
16.10.2

Mea Mansuetudo

12.1.121

Nostra Moderatio

6.30.24
8.18.3

Nostrum Numen

1.2.12
1.9.2
2.23.1
2.33.4
5.12.3
5.12.3
6.4.29
6.4.32
6.5.2
6.14.3
6.23.3
6.30.15
7.7.4
7.8.3
8.1.13
8.5.40
8.5.62
9.40.11
11.21.3
11.28.15
11.30.49
12.12.7
15.4.1
15.5.5
16.4.4
16.8.13

Meum Numen

11.1.33

Nostra Perennitas

1.1.5
2.4.4
4.4.5
5.15.18
7.7.4
9.19.3
9.38.8
10.20.10
12.12.9
13.5.12
15.1.31

Mea Perennitas

6.30.21

Nostra Pietas

5.12.3
6.10.1
10.26.1
11.1.34
11.1.36
13.1.21
14.26.2
15.1.37

Mea Pietas

14.16.2

Nostra Serenitas

1.1.2
1.12.5
1.22.2
2.16.2
4.4.3
5.13.2
5.16.31
6.8.1
6.22.3
6.23.1
6.26.13
6.27.8
6.29.3
6.30.17
7.1.17
7.8.10
8.5.14
8.5.22
8.5.32
8.5.45
8.5.48
8.5.56
8.7.16
9.19.3
9.38.6
9.38.9
9.40.7
9.40.20
9.42.14
9.42.19
9.42.20
10.10.11
11.2.5
11.16.20
11.28.4
11.30.47
11.30.56
11.30.64
11.31.9
11.31.9
12.13.6
13.10.8
14.2.1
14.4.8
15.1.11
15.1.26
15.1.42
15.1.51
15.5.5
15.7.6
15.7.6
16.2.37
16.5.12
16.5.14
16.8.22
16.11.3

Mea Serenitas
11.20.5

Nostra Scientia

1.1.5
1.5.1
1.15.2
1.16.6
1.29.1
2.18.1
6.4.21
7.1.12
8.5.25
9.1.1
9.1.13
9.4.1
9.21.1
9.34.3
10.8.3
11.7.16
11.16.8
11.16.8
11.29.2
11.30.1
11.30.1
11.30.9
11.30.18
11.30.18
11.37.1
12.1.1
12.12.3
15.1.2
15.1.2
15.1.30
16.10.1
16.10.15

Nostra Tranquillitas

1.2.10
1.6.4
5.15.18
6.4.31
6.12.1
8.7.16
11.30.31
16.1.4
16.2.15
16.4.1

Plan for Final Research Project

For the final research, I am going to analyze the works of the early 20th century Japanese writers. I want to choose two to three authors from Natsume Soseki, Dazai Osamu, Tanizaki Junichiro and Akutagawa Ryunosuke, so the project will not be too overwhelming. Many of their work are available on Aozora Bunko, and I have read at least one of their work.

Historiographical Research

The Data for Research of JSTOR is not a perfect tool for historiographical research on Japanese literature. My search for the keyword “Nastume Soseki” gives back a two documents on Shakespeare in 2000 and 2001.

These outliers, however, are not going to seriously affect the study, since I am only interested in counting word frequency and I have a large data set. The code I used is from class. I wrote some new code for graphing and picking frequent word from the document.

The key term “Nastume Soseki” yields a result of 697 documents. For the vertical axis in all the following graphs, I used rolling mean of percentage of the word over five years, since it gives the most smooth graph.

Group 1: Translation

Since the majority of documents from JSTOR are in English, I expected many documents to discuss translation. The first group of keywords that I looked for is “translation” and names of three famous translators.The graph shows that the study of Nastume Soseki’s work in translation rise around 1950. It makes sense, since most of his work was translated after World War II. There are only a few document before 1950 in the result. The key term “translation”, although with some fluctuation, is always important after 1950. Three other keywords “McClellan”, “Keene” and “Seidensticker”, translators’ names, appeared more from 1950 to 2000. The three authors were all born in 1920s, so their works concentrated in the late 20th century.

Group 2: Language

The keyword “Japanese” appeared dominant as expected. Because most of the documents are from Asian studies journals, “Chinese” and “Korean” appears frequently. The line of “English” is close to the line of “Chinese”. If most of works are about translation, the word “English” would appear more. Therefore, there maybe a large portion of the work that does not directly discuss translation; these documents are probably about general literature or cultural study.

GROUP 3: Theme

The four key words in these graph are “death”, “love”, “moral” and “war”. “Love” and “war” are more prevalent. “War” also appears in works published during WWII, and have several peaks. I do not remember reading a lot about war in Natsume Soseki’s works, but scholars might what to find what is the connection between pre-war literature and WWII. I find “war” is similarly a dominant key term in the search for “Dazai Osamu” in DfR in JSTOR, although most of his work are not related to war.

Group 4: IMPLICATION AND CONNECTION

Here, I am interested in how scholars interpreted Natsume’s works and their political, social, economic and historical connections. “Political” and “social” are closely related, since they move together. “Economic” has a falling importance, while “historical” appeared to be more import along the timeline.

GROUP 5: AUTHORS

The search for “Natsume Soseki” in JSTOR does not return documents exclusively about Natsume Soseki. Some documents about other Japanese authors may also appear. The graph above shows that “natsume” was constantly above other authors, except years aroud 1965 and 2000, when “akutagawa” has two peaks. The truth is that “akutagawa” appears in total 109 times from 1968 to 1972 and 153 times in 2004. The data set is not perfect, but it will not cause serious bias.

The graph also shows the correlation between authors. Three of the authors, “murasaki”, “chikamatsu” and “matsuo”, are not from the 20th century. Their lines are in green and blue and do not raise much from 0. Modern authors, whose lines are in orange and red. The line for “tanizaki”, “dazai”and “kawabata” are close together.

Similar Graph for the Search of Dazai Osamu

“Keene” is more important among the three translator. He translated Dazai’s “No Longer Human”.

“War” is also a dominant theme, but the peaks around 1960 and 2000 are somewhat different in time from the previous graph from Nastume Soseki.

This graph looks better, since “dazai” is more dominant.

Google Ngram

Google Ngram is easy to use and its results are interesting.

All five are the 20th century Japanese writers. From this graph, we can see the frequency increased from 1950, and have two peaks at 1970s and 1990s. This partially match the graph of Dazai, but is different from the graph of Natsume from JSTOR.

Three ancient writers, Murasaki (11th c.), Matsuo(17th c.) and Chikamatsu(17th c.) do not follow the pattern of 20th century writers.

Contemporary writers, Murakami (1949 – ) do not follow the pattern as well.

Part of the code for PLOtting

I have difficulty changing the order of the legends, but everthing else works fine.

keepers <- c("japanese","english","chinese","korean")
Tokugawa.full.smaller <- Tokugawa.full.perc.df[,keepers]
Tokugawa.full.smaller[is.na(Tokugawa.full.smaller)] <- 0
Tokugawa.smaller.roll.5 <- data.frame(rollmean(Tokugawa.full.smaller, k=5, fill = list(NA, NULL, NA)))
Tokugawa.smaller.roll.5$pubyear <- Tokugawa.full.perc.df$pubyear
mathching <- c("japanese" = "black","english" = "blue","chinese" = "red","korean" = "green")
ggplot(Tokugawa.smaller.roll.5, aes(x=pubyear)) + 
 geom_line(aes(y = japanese, color = "japanese")) +
 geom_line(aes(y = english, color = "english"))+
 geom_line(aes(y = chinese, color = "chinese")) +
 geom_line(aes(y = korean, color = "korean")) +
 scale_colour_manual(name="Keywords",values = mathching)+
 xlab("Year") + ylab("Rolling Mean of Percentage over Five Years") 

For this week’s blog post, please click here.

There are two versions of the draft of Ping Ding Hai Kou Fang Lue: the Manchu and Chinese versions. If the Manchu and Chinese versions were translated from each other, the two versions should be exactly the same. However, as known, they are different. How different are they? The Manchu language and Chinese are linguistically different, so it is impossible to analyze grammar, sentence structure, and writing style. However, it is possible to analyze proper nouns. In this text, there are three primary proper nouns: toponyms, people’s names, and position titles. I thus analyze the difference of percentage between two versions in proper nouns, including places, people’s name, and position titles, and Dunning log-likehood as well as tf-idf of six overlapping places in all three volumes. By understanding the result, I can check the text to deeply recognize the difference between two versions.

Graph 1: Percentage of Manchu minus percentage of Chinese text mines in Vol. 1

Graph 2: Percentage of Manchu minus percentage of Chinese text mines in Vol. 2

Graph 3: Percentage of Manchu minus percentage of Chinese text mines in Vol. 3

Graph 4: Percentage of Manchu minus percentage of Chinese text mines in three volumes

Graph 1 to Graph 4 represent the difference that the percentage of text mines in places in Manchu language minus the text mines in places in Chinese language from Volume 1 to Volume 3 and all three volumes. Graph 1 suggests that Fujian is much more frequent in Manchu than in Chinese. Dutch is more frequent in Chinese than in Manchu. Graph 2 shows that Fujian, Penghu, and Haitan are more frequent in Manchu than in Chinese. Additionally, Xiamen and Meizhou are more frequent in Chinese than in Manchu. Graph 3 suggests that Taiwan is much more frequent in Manchu than in Chinese. By contrast, Penghu is more frequent in Chinese than in Manchu. Overall, Fujian and Taiwan are more frequent in Manchu than in Chinese, and Dinghai, Dutch, and Pinghai are slightly more frequent in Chinese than in Manchu. Among these places, Fujian is easily to explain. In Chinese, each province has its own abbreviation; for example, Min is the abbreviation of Fujian. The percentage of frequency of Dutch in Chinese volume 1 is more different than in Manchu volume 1 because there is one paragraph, which accounts Dutch navy supported the Qing, describes differently in two versions.

Graph 5: Percentage of Manchu minus percentage of Chinese text mines in Vol. 1

Graph 6: Percentage of Manchu minus percentage of Chinese text mines in Vol. 2

Graph 7: Percentage of Manchu minus percentage of Chinese text mines in Vol. 3

Graph 8: Percentage of Manchu minus percentage of Chinese text mines in three volumes

By using the similar approach to analyze people’s name, it can obtain the result of difference between two versions. However, in this part, I make a slight change. Instead of using entire Chinese name, combined by first and last name, I only search people’s first name because it is more common to use first name only in text. More importantly, because certain surname, such as Wang, also refers a noble rank in Chinese and Manchu, it caused to confused once I analyzed entire Chinese name. Therefore, in order to avoid the misunderstanding, it is appropriate to only search first name. Graph 5 suggests that some people’s names in Manchu never appear in Chines, and people who are more frequently mentioned in Manchu are Manchu people. Oppositely, Wan Zhengse, a military commander, appear more frequent in Chinese than in Manchu. Graph 6 also suggests the difference between two texts. Again, Wan Zhengse is still more frequent mentioned in Chinese than in Manchu, and people who are more frequently mentioned in Manchu are Manchu people. Interestingly, Graph 7 shows the similar result that people who are more frequently mentioned in Manchu are Manchu people. Noticeably, Manchu people are more frequently mentioned in Manchu, and Chinese people, including Hanjun Bannersmen and Han Chinese, are more frequently mentioned in Chinese.

Graph 9: Percentage of Manchu minus percentage of Chinese text mines in Vol. 1

Graph 10: Percentage of Manchu minus percentage of Chinese text mines in Vol. 2

Graph 11: Percentage of Manchu minus percentage of Chinese text mines in Vol. 3

Graph 12: Percentage of Manchu minus percentage of Chinese text mines in three volumes

Finally, using the same process to analyze the position title, such as governors (dzungdu), commanders (tidu), and generals (jiangjun), Graph 9, Graph 10, Graph 11, and Graph 12 show that viceory, marshal, and general are more frequently mentioned in Manchu than Chinese. The main reason is because these three terms are able to be replaced by other abbreviations in Chinese. Usually, in Chinese, authors prefer to use the abbreviations to refer these position titles. However, this also points out that Manchu language text indicated precisely and directly.

The first analysis about prop nouns is regarding places. Drawing the results of Graph 1 to Graph 4 on map can provide precise visual sense. Graph 13 to 16 shows the result. In some degree, Graph 13 to Graph 15 display shift over time, and Graph 16 shows the completed shift over time showing in three volumes.

Graph 13: the percentage difference in volume 1

Graph 14: the percentage difference in volume 2

Graph 15: the percentage difference in volume 3

Graph 16: the percentage difference in three volumes

However, mapping statistic results is questionable. In order to provide more precise result, two methods can be used: Dunning’s log-likehood and tf-idf. Dunning’s log-likhood offers an efficient approach to compare two texts. When the value of Dunning’s log-likhood (G²) is 15.13, the significance vale of p is less than 0.0001 (p<0.0001). Then, when G² is 10.83, p is less than 0.001. When G2 is 6.63, p is less than 0.01. When G2 is 3.84, p is less than 0.05. As a result, Table 1 suggests that Fujian shows the significant difference in the first three volumes, but Zhejiang, Taiwan, Xiamen, Jinmen, and Haicheng were similar based on the statistic method.

Table 1: The difference of the six overlapping places in the first three volumes by using Dunning’s log-likehood. The analysis text is the Manchu volumes, and the reference text is the Chinese volumes.

As analysis above, these six places show difference changes. For Fujian, the difference was less and less significant, but it was still the most different in the first three volumes. Conversely, Zhejiang and Taiwan became more and more different although it was not significant difference based on the Dunning’s log-likhood. Xiamen, Jinmen, and Haicheng did not suggest the significant difference in the first three volumes.

Besides the Dunning’s log-likehood, another effective approach of text mining is tf-idf (term frequency–inverse document frequency). By using the tf-idf approach, the value of six places in these two language versions are showed in Table 3.

Table 3: tf-idf in three volumes in Manchu and Chinese.

What can the statistics show? The statistics can at least tell readers two facts. First, as mentioned above, categorizing them in two clusters, the large scale cluster including Fujian, Zhejiang, and Taiwan, shows that they became increasing different. Additionally, the Manchu version might describe large places more precise than Chinese version did, but both equally described the city or small scale places.

Why did Fujian decrease its difference over time? Comparing to the comparison of city scale, the government focuses on cities because the war between the Qing and Zheng had become locally. This fully explains why a lot of cities, towns, and villages appeared in the second volume. As a result, either Chinese or Manchu recorded the similar tendency because they were probably written based on the same sources.

According to the analyses, a lot of differences are obvious. For example, Manchu people are more frequently mentioned in Manchu version; by contrast, Han Chinese are more frequently mentioned in Chinese version. Additionally, by analyzing Dunning’s log-likehood and tf-idf, the six overlapping places in all three volume suggests that the importance of places change over time. Although the two versions are basically similar in their structures and archives, they are significantly different. Consequently, the Manchu and Chinese versions are not translated from each other. After comparing three major proper nouns– place, person, and position, it suggests that the Manchu version is more precise than Chinese.