Author: Cheng-Heng Lu

The difference of percentage (provinces scale) between Manchu and Chinese version on map combines the line of the Coastal Exclusion Policy.

The difference of percentage (cities scale) between Manchu and Chinese version on map combines the line of the Coastal Exclusion Policy.

Coding is below.

##————————————————————————————————–##

rm(list=ls())
fileEncoding= “UTF-8”

## read file
setwd(“~/Desktop/003_PhD/016_Coursework/003_2016 Fall/003_HIST582A/003_Text”)
library(stringr)

## scan Chinese and Manchu texts
Chinese.vol.1.txt <- scan(“PDHF_Chinese_1.txt”, what = “chr”)
Chinese.vol.2.txt <- scan(“PDHF_Chinese_2.txt”, what = “chr”)
Chinese.vol.3.txt <- scan(“PDHF_Chinese_3.txt”, what = “chr”)
Manchu.vol.1.txt <- scan(“PDHF_Manchu_1.txt”, what = “chr”)
Manchu.vol.2.txt <- scan(“PDHF_Manchu_2.txt”, what = “chr”)
Manchu.vol.3.txt <- scan(“PDHF_Manchu_3.txt”, what = “chr”)

##————————————————————————————————–##
## [toponym counts] ##
## read table of place names in Chinese, Manchu, and English
Ch.place.names <- read.table(“Chinese_place_names.txt”, stringsAsFactors = FALSE)
Man.place.names <- read.table(“Manchu_place_names.txt”, sep=”\t”, stringsAsFactors = FALSE)
Eng.place.names <- read.table(“English_place_names.txt”, sep=”\t”, stringsAsFactors = FALSE)

## creating a new colname
Man.place.names$places <- tolower(Man.place.names$V1)
Ch.place.names$places <- tolower(Ch.place.names$V1)
Eng.place.names$places <- tolower(Eng.place.names$V1)
Ch.toponym <- unique(Ch.place.names$V1)
Man.toponym <- unique(Man.place.names$places)
Eng.toponym <- unique(Eng.place.names$V1)

## paste the full text
Manchu1 <- tolower(paste(Manchu.vol.1.txt, collapse = ” “))
Manchu2 <- tolower(paste(Manchu.vol.2.txt, collapse = ” “))
Manchu3 <- tolower(paste(Manchu.vol.3.txt, collapse = ” “))
Chinese1 <- paste(Chinese.vol.1.txt, collapse = “”)
Chinese2 <- paste(Chinese.vol.2.txt, collapse = “”)
Chinese3 <- paste(Chinese.vol.3.txt, collapse = “”)

## make the full Chinese text as a dataframe
Ch.Texts.df <- rbind.data.frame(Chinese1, Chinese2, Chinese3, stringsAsFactors = FALSE)
Chinese_all <- paste(Ch.Texts.df, collapse =””)
Ch.Texts.df <- rbind.data.frame(Chinese1, Chinese2, Chinese3, Chinese_all, stringsAsFactors = FALSE)

## rename the colname
colnames(Ch.Texts.df) <- “texts”

Ch.Text.metrics <- data.frame(t(data.frame(lapply(Ch.toponym, FUN=function(x) str_count(Ch.Texts.df$texts, x)))))

## put the place as one colname
Ch.Text.metrics$places <- Ch.toponym

## make three colnames sequently
colnames(Ch.Text.metrics)[c(1:4)] <- c(“Chinese1”, “Chinese2”, “Chinese3”, “Chinese_all”)

## the same process of Chinese in the Manchu version
Man.Texts.df <- rbind.data.frame(Manchu1, Manchu2, Manchu3, stringsAsFactors = FALSE)
Manchu_all <- tolower(paste(Man.Texts.df, collapse = ” “))
Man.Texts.df <- rbind.data.frame(Manchu1, Manchu2, Manchu3, Manchu_all, stringsAsFactors = FALSE)
colnames(Man.Texts.df) <- “texts”

Man.Text.metrics <- data.frame(t(data.frame(lapply(Man.toponym, FUN=function(x) str_count(Man.Texts.df$texts, x)))))

Man.Text.metrics$places <- Man.toponym
colnames(Man.Text.metrics)[c(1:4)] <- c(“Manchu1”, “Manchu2”, “Manchu3”, “Manchu_all”)

## combine Chinese and Manchu dataframe together
Combined.df <- cbind.data.frame(Ch.Text.metrics, Man.Text.metrics)
Combined.df$Chinese1.perc <- Combined.df$Chinese1/sum(Combined.df$Chinese1)*100
Combined.df$Chinese2.perc <- Combined.df$Chinese2/sum(Combined.df$Chinese2)*100
Combined.df$Chinese3.perc <- Combined.df$Chinese3/sum(Combined.df$Chinese3)*100
Combined.df$Chinese_all.perc <- Combined.df$Chinese_all/sum(Combined.df$Chinese_all)*100
Combined.df$Manchu1.perc <- Combined.df$Manchu1/sum(Combined.df$Manchu1)*100
Combined.df$Manchu2.perc <- Combined.df$Manchu2/sum(Combined.df$Manchu2)*100
Combined.df$Manchu3.perc <- Combined.df$Manchu3/sum(Combined.df$Manchu3)*100
Combined.df$Manchu_all.perc<- Combined.df$Manchu_all/sum(Combined.df$Manchu_all)*100

## show the result
Combined.df$toponym <- paste(Combined.df[,10], Combined.df[,5], Eng.place.names$places, sep=” “)
Combined.df$toponym

##————————————————————————————————–##
## [Coastal exclusion policy] ##
library(ggmap)
chinastate.map<-get_map(location=”china”, zoom=10, maptype=”satellite”)

cities.cep<- c(“廣西壯族自治區欽州市”, “廣西壯族自治區北海市合浦縣”, “合浦县石城村”, “廣東省湛江市遂溪县乾留”,
“湛江市雷州市海康港”, “湛江市雷州市扶茂”, “廣東省湛江市徐聞縣”, “廣東省湛江市徐聞縣海安鎮”,
“廣東省湛江市雷州市深田村”, “廣東省湛江市雷州市”, “廣東省湛江市遂溪縣”, “廣東省湛江市遂溪縣長坡墩”,
“廣東省湛江市吴川市博茂”, “廣東省湛江市吳川市”, “廣東省茂名市電白區”,
“廣東省陽江市陽西縣雙魚村”, “廣東省陽江市”, “江門市恩平市”, “廣東省江門市開平市”, “新會區將軍山旅遊區”,
“廣東省江門市新會區崖門鎮”, “廣東省江門市新會區”, “新會區觀音山”, “佛山市順德區”, “中山市三角鎮”,
“中山市馬鞍村”, “南沙区小虎山”, “深圳市寶安區西鄉”, “深圳市大鵬所城”, “海丰县琵琶”, “廣東省汕尾市海豐縣”,
“揭陽市惠來縣”, “揭陽市惠來縣靖海鎮”, “广东省汕头市潮南区古埕”, “廣東省汕頭市潮陽區”,
“揭陽市揭東區鄒堂”, “廣東省揭陽市”, “廣東省潮州市”, “潮州市饒平縣”, “福建省漳州市詔安縣分水關”,
“福建省漳州市詔安縣”, “漳州市云霄县油甘公”, “漳州市漳浦縣”, “漳州市漳浦縣橫口圩”, “漳州市龙海市洪礁寨”,
“漳州市龍海市海澄鎮”, “福建省漳州市龍文區江東橋”, “廈門市同安區蓮花村”, “廈門市同安區”, “廈門市翔安區小盈嶺”,
“福建省泉州市南安市大盈”, “福建省晉江”, “福建省泉州市南安市”, “泉州市洛江区洛陽橋”,
“泉州市惠安县石任”, “泉州市泉港区九峰山”, “莆田市荔城區壺公山”, “莆田市涵江區江口鎮”, “福清市高嶺村”,
“福州市福清市”, “長樂市岐陽村”, “馬尾區閩安村”, “福州市連江縣”, “連江縣浦口鎮”, “蕉城區白鶴嶺”,
“福建省寧德市”, “福安市洋尾”, “福安市小留村”, “寧德市福安市”, “福鼎市沙埕鎮”)
geo.cities.cep <- geocode(cities.cep)
geo.cities.cep.df<- data.frame(geo.cities.cep)

ggmap(chinastate.map) + geom_point(data=geo.cities.cep, aes(x=lon, y=lat))+ xlim(c(108, 122)) +ylim(c(20,28))

##————————————————————————————————–##
## [map] ##
library(ggmap)
china.map<-get_map(location=”China”, zoom=10, maptype=”satellite”)

cities<- c(“福建省泉州南安市安平橋”, “福建省廈門市同安區丙洲”, “湖南省長沙市”, “廣東省潮州市”,
“中國福建省泉州市惠安縣崇武鎮”, “中國福建省三明市永安市大漳山”, “福建福州市連江縣定海古城”,
“印尼雅加達”, “福建省寧德市霞浦縣烽火島”, “中國福建省”, “福建省福州市”, “福建省福州市長樂市新塘”,
“江蘇省揚州市邗江區瓜洲鎮”, “中國廣東省”, “中國貴州省”, “福建省漳州市龍海市海澄鎮”,
“福建省福州市平潭縣海壇島”, “福建省福州市台江區河口新村”, “中國湖北省”, “廣東省惠州市”,
“江蘇省南京市”, “廣東省汕尾市陸豐市碣石鎮”, “中國江蘇省”, “金門縣”, “廣東省汕頭市濠江區馬滘”,
“福建省莆田市秀嶼區湄洲大道湄洲島”, “福建省福州市馬尾區閩安村”, “廣東省汕頭市南澳縣”,
“浙江省寧波市”, “普列莫爾斯基區海參崴”, “廣東省汕頭市龍湖區鷗汀”, “臺灣省澎湖”,
“福建省莆田市秀嶼區平海鎮”, “浙江省溫州市平陽縣”, “福建省泉州市”, “浙江省紹興市”,
“福建福州市平潭縣石牌洋”, “福建省泉州石井鎮”, “福建省泉州市惠安縣獺窟島”, “浙江省台州市”,
“臺灣台南”, “福建省龍岩市長汀縣汀州”, “福建省廈門市同安區”, “福建省漳州市東山縣”,
“福建省泉州市惠安縣”, “福建省泉州市晉江市圍頭村”, “浙江省溫州市”, “福建省漳州市龍海市浯嶼”,
“福建省廈門市翔安區斗門”, “福建省廈門市”, “福建省莆田市”, “福建省廈門市集美區潯尾”,
“福建省泉州市石獅市永寧鎮”, “湖南岳陽縣”, “福建省漳州市雲霄縣”, “福建省漳州市”,
“中國浙江省”, “江蘇省鎮江市”, “浙江省舟山市”, “福建省泉州石獅市”)

geo.cities <- geocode(cities)
geo.cities.df<- data.frame(geo.cities)

map.df <- cbind.data.frame(Combined.df, geo.cities.df)

library(maps)
library(mapdata)
library(ggplot2)
world.map<- borders(database=”world”)
ggplot()+ world.map+ coord_quickmap()

world.map<- borders(database=”world”, colour=”gray20″, fill=”gray60″)
ggplot() + world.map +
coord_map(projection = “gilbert”, xlim =c(100,140), ylim=c(-20,50)) +
xlab(“”) + ylab(“”)+ ggtitle(“Percentage difference map”)
##————————————————————————————————–##
## [The new try] ##
## mixed provinces and cities ##
## volume 1 ##
Combined.df$perc1.diff <- (Combined.df$Chinese1.perc – Combined.df$Manchu1.perc)
Combined.df$perc2.diff <- (Combined.df$Chinese2.perc – Combined.df$Manchu2.perc)
Combined.df$perc3.diff <- (Combined.df$Chinese3.perc – Combined.df$Manchu3.perc)
map.df <- cbind.data.frame(Combined.df, geo.cities.df)
map.df$type1 <- ifelse(map.df$perc1.diff>0, “Chinese”, “Manchu”)
map.df$type1 <- ifelse(map.df$perc1.diff == 0, NA, map.df$type1)
map.df$type2 <- ifelse(map.df$perc2.diff>0, “Chinese”, “Manchu”)
map.df$type2 <- ifelse(map.df$perc2.diff == 0, NA, map.df$type2)
map.df$type3 <- ifelse(map.df$perc3.diff>0, “Chinese”, “Manchu”)
map.df$type3 <- ifelse(map.df$perc3.diff == 0, NA, map.df$type3)
map.df$scale <- c(“city”, “city”, “city”, “city”, “city”, “city”, “city”, “province”, “city”,”province”, “city”, “city”, “city”,
“province”, “province”, “city”, “city”, “city”, “province”, “city”, “city”, “city”, “province”, “city”, “city”,
“city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”,
“city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”, “city”,
“city”, “city”, “city”, “province”, “city”, “city”, “city”)

## only province in Manchu and Chinese##
## volume 1 ##
vol1bp<- ggplot() + world.map + geom_point(data = subset(map.df, scale== “province”), aes(x = lon, y = lat, color=type1, shape = scale, size=abs(perc1.diff))) +
guides(size = FALSE) +
geom_path(data = geo.cities.cep.df, aes(x = lon, y = lat, color = “Coastal Exclusion Policy”))+
coord_map(projection = “stereographic”, xlim = c(112, 123), ylim = c(21,34)) + ylab(“”) + xlab(“”) +
ggtitle(“Provinces in Volume 1”)

## volume 2 ##
vol2bp<- ggplot() + world.map + geom_point(data = subset(map.df, scale == “province”), aes(x = lon, y = lat, color=type2, shape = scale, size=abs(perc2.diff))) +
guides(size = FALSE) +
geom_path(data = geo.cities.cep.df, aes(x = lon, y = lat, color = “Coastal Exclusion Policy”))+
coord_map(projection = “stereographic”, xlim = c(112, 123), ylim = c(21,34)) + ylab(“”) + xlab(“”) +
ggtitle(“Provinces in Volume 2”)

## volume 3 ##
vol3bp<- ggplot() + world.map + geom_point(data = subset(map.df, scale == “province”), aes(x = lon, y = lat, color=type3, shape = scale, size=abs(perc3.diff))) +
guides(size = FALSE) +
geom_path(data = geo.cities.cep.df, aes(x = lon, y = lat, color = “Coastal Exclusion Policy”))+
coord_map(projection = “stereographic”, xlim = c(112, 123), ylim = c(21,34)) + ylab(“”) + xlab(“”) +
ggtitle(“Provinces in Volume 3”)

##only cities in Manchu and Chinese##
## volume 1##
vol1bc<- ggplot() + world.map + geom_point(data = subset(map.df, scale== “city”), aes(x = lon, y = lat, color=type1, shape = scale, size=abs(perc1.diff))) +
guides(size = FALSE) +
geom_path(data = geo.cities.cep.df, aes(x = lon, y = lat, color = “Coastal Exclusion Policy”))+
coord_map(projection = “stereographic”, xlim = c(112, 123), ylim = c(21,34)) + ylab(“”) + xlab(“”) +
ggtitle(“Cities in Volume 1”)

## volume 2 ##
vol2bc<- ggplot() + world.map + geom_point(data = subset(map.df, scale== “city”), aes(x = lon, y = lat, color=type2, shape = scale, size=abs(perc2.diff))) +
guides(size = FALSE) +
geom_path(data = geo.cities.cep.df, aes(x = lon, y = lat, color = “Coastal Exclusion Policy”))+
coord_map(projection = “stereographic”, xlim = c(112, 123), ylim = c(21,34)) + ylab(“”) + xlab(“”) +
ggtitle(“Cities in Volume 2”)

##volume 3 ##
vol3bc<- ggplot() + world.map + geom_point(data = subset(map.df, scale== “city”), aes(x = lon, y = lat, color=type3, size=abs(perc3.diff))) +
guides(size = FALSE) +
geom_path(data = geo.cities.cep.df, aes(x = lon, y = lat, color = “Coastal Exclusion Policy”))+
coord_map(projection = “stereographic”, xlim = c(112, 123), ylim = c(21,34)) + ylab(“”) + xlab(“”) +
ggtitle(“Cities in Volume 1”)
## this function is from http://kanchengzxdfgcv.blogspot.tw/2016/11/r-ggplot2.html##
multiplot <- function(…, plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
plots <- c(list(…), plotlist)
numPlots = length(plots)
if (is.null(layout)) {
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}

if (numPlots==1) {
print(plots[[1]])

} else {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
for (i in 1:numPlots) {
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
## provinces in both##
multiplot(vol1bp, vol2bp, vol3bp, cols= 1)

## cities in both##
multiplot(vol1bc, vol2bc, vol3bc, cols =1)

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.

Place	Volume 1	Volume 2	Volume 3
Fujian	15.491	4.5189	5.764
Zhejiang	0.009	1.130	1.590
Taiwan	0.009	0.052	0.498
Xiamen	0.476	0.891	0.384
Jinmen	0.294	0.269	0.384
Haicheng	0.072	0.154	0.128

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.

Place		Volume 1			Volume 2			Volume 3
		tf	idf	Tf-idf	tf	idf	Tf-idf	tf	idf	Tf-idf
Fujian	M	0.487	0.720	0.350	0.2	1.610	0.322	0.363	1.012	0.368
	C	0.571	0.560	0.320	0.2	1.610	0.322	0.375	0.981	0.368
Zhejiang	M	0.128	2.054	0.263	0.05	2.996	0.150	0.045	3.091	0.141
	C	0.071	2.640	0.189	0.05	2.996	0.150	0.063	2.772	0.173
Taiwan	M	0.064	2.747	0.176	0.225	1.492	0.336	0.432	0.840	0.054
	C	0.071	2.640	0.189	0.2	1.609	0.322	0.344	1.068	0.076
Xiamen	M	0.167	1.792	0.299	0.25	1.386	0.347	0.068	2.686	0.183
	C	0.143	1.946	0.278	0.275	1.291	0.355	0.094	2.367	0.222
Jinmen	M	0.141	1.959	0.276	0.175	1.743	0.305	0.068	2.656	0.183
	C	0.125	2.079	0.260	0.175	1.743	0.305	0.094	2.367	0.222
Haicheng	M	0.013	4.357	0.056	0.1	2.303	0.230	0.023	3.784	0.086
	C	0.018	4.025	0.072	0.1	2.303	0.230	0.031	3.466	0.108

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.

1.   Introduction, Historiography, and Methodology

Is the Manchu language source merely the copy of Chinese source? Does the Manchu language source matter for studying Qing history? The question has been debated for over one century. In this article, I propose to argue that the Manchu language source not only matters but also is at least equally important as Chinese sources.

The oral Manchu language was used by Northeastern China, as known as Manchuria. In 1587, Nurgaci established a regime, and became khan of this area in 1589. In 1616, Nurgaci created a national title, Jin. During this period, because the government requested a more systematic writing so as to enhance the political efficiency, Erdeni and G’agai created the Manchu language based on the Mongolian linguistic system. This Manchu language writing system had limitation to spell non-Manchu language names or places, and, the most importantly, this writing system could not distinguish the sound k, g, and h. Comparing to the later revised Manchu language, this writing system was called the Old Manchu language.

In 1632, Hong Taiji, Nurgaci’s son, asked Dahai to modify the Old Manchu language. The new writing system included ten new words in order to spell names and places, clarified the difference between k, g, and h, and standardized the writing system. Therefore, for about 30 years, the Manchu language was mature enough to become a standard language to use. When the Qing occupied China, the Manchu language became the official language for all regions within the empire, including China, Mongolia, Tibet, and Uyghur until 1911.

In the early 20th century, Japanese scholars had noticed the importance and specialty of the Manchu language. Using the Manchu language sources to study Qing history had become more and more important in Japan. On the contrary, in China, although some scholars understand the Manchu language, using the Manchu language sources to study Qing history did not become a primary research approach at all. There are at least three main reasons.

First, because of Sinization, a lot of scholars did not pay attention on the Manchu language. For these scholars, in the same document, the Manchu language part was just translated from the Chinese part. Second, the amount of Chinese sources is the way more than the amount of the Manchu language sources. As a result, it is not necessary to read the Manchu language. Third, for them, the Manchu language was likely less important after the High Qing, and, meanwhile, ministers’ capacity of using Manchu language had gradually disappeared. As a result, because of these three reasons, the Manchu language sources had not been emphasized for a long time.

In 2004, a new historiographic approach appeared. This historiographic approach is called the New Qing History or the New Qing Imperial History. Over all, the New Qing History proposes to understand Qing history based on three new concepts. First, the New Qing History refuses the Sino-centrism, but, must be clarified, the New Qing History also does not entirely ignore the importance of Sinization. Instead of Sinicization, the New Qing History emphasizes the Manchu elements of the Qing Empire. Second, since the Qing Empire was not a Sinicized empire, the Qing Empire must have its unique. In this context, the New Qing History notices that the Qing Empire was in fact an empire as same as other empires in early modern period, such as the British Empire, Russia Empire, and Ottoman Empire. In other words, the Qing Empire was not a Chinese Empire but a universal empire, and China was just a part of this empire. Third, since the New Qing History emphasizes the importance of the Manchu element, the most direct approach to engage with the Manchus is widely using the Manchu language sources. For the New Qing historians, the Manchu language source is independent instead of a translation copy of Chinese part. Admittedly, the New Qing History generates considerable meaningful results and works, but increasing opponents still judge the three main concepts. One of the most common comments is that the New Qing historians overemphasize the importance of the Manchu language sources in an exaggerative way.

Based on this historiographic debate, this article analyzes a text in Chinese and the Manchu language. The text is Ping Ding Hai Kou Fang Lue (the Book of Strategic Record about Suppressing the Pirate, 平定海寇方略). Fang Lue was a literal form in the Qing period, and this form was only used by the government. When the Qing Empire defeated an enemy, the government edited a book for recording every detail chronologically based on official archives. Because Fang Lue was not only a book recording historical events but also a book proclaiming imperial victory, authority, and prestige, it is reasonable that the book should be edited in to multiple languages. So far, as we known, there were 25 Fang Lue. Among these 25 Fang Lue, Ping Ding Hai Lou Fang Lue was the only one which had not been found the completed version. In the past century, the Chinese version of this Lang Lue was the only version. Noticeably, this Chinese version was just a draft with four volumes. In 2011, I discovered the Manchu language version in the Grand Council Archive. This Manchu language version was also a draft, and it only had the first three volumes. Even though the Manchu volume only included the first three volumes, the Chinese version and the Manchu language version were still comparable because of three reasons. First, they overlapped the first three volumes. Second, they were edited at the same time. Third, they recorded the same event. Therefore, by comparing these two texts, this article seeks the relationship between the Chinese and the Manchu language versions.

As can be seen in Table 1, the Manchu and Chinese texts cover the exactly same period. In other words, these two texts record same events. In fact, this makes sense. Since the main purpose of this book is to record history and proclaim imperial prestige, the two texts should therefore have the same content. However, since the two texts should be in literal the same, it is interesting if there is any tiny difference.

Table 1: The period covered in the first three volumes

	Time	Chinese source	Manchu language source
Volume 1	Beginning	March 1679	March 1679
	End	December 1679	December 1679
Volume 2	Beginning	March 1680	March 1680
	End	August 1680	August 1680
Volume 3	Beginning	March 1681	March 1681
	End	November 1682	November 1682

 

This article uses digital analysis to do text mining. The first problem encountered is the difference between two languages in grammar, writing system, and meaning. Because Chinese and the Manchu language are linguistically different, it is difficult, or impossible, to compare words by words. Fortunately, as mentioned above, since the two texts records the same events based on the same sources during the same time, the amount of the proper nouns and the name of places had to be matched. As a result, I propose to compare the amount of the name of places in two texts to see whether the two texts were translated or copied from the other. Then, I seek to individually map the name of places mentioned in two texts, and, by combining the geographic, political, and environmental phenomenon, I try to look for a big picture regarding the difference of the two texts.

2.   The Comparison of Two Texts

Table 2 suggests that, besides the term of “Dutch,” the rest name of places appeared more frequent in the Manchu language than in Chinese sources in the volume 1. It is hard to say whether the Manchu language text is more precise than Chinese text. However, this suggests that the Manchu language text and Chinese text are different. Table 3 suggests that the frequency of name of places in the Manchu language text is more than in the Chinese text. Nevertheless, the frequency of Kimmen, Nan’ao, Pinghai, and Tongshan are the same in both language texts. As a result, the two texts are different.

Table 2: The Frequency of the name of places in the Volume 1

Order	Name of places	Manchu texts	Frequency	Chinese texts	Frequency
1	Fujian	fugiyan	38	福建	20
2	Xiamen	hiya men	14	廈門	8
3	Kimmen	gin men	11	金門	7
4	Dutch	ho lan	11	荷蘭	11
5	Tingzhou	ting jeo	8	汀州	2
6	Taiwan	tai wan	5	臺灣	4
7	Zhangzhou	jang jeo	5	漳州	3
8	Youzhou	yo jeo	5	岳州	5
9	Chaozhou	coo jeo	5	潮州	4
10	Quanzhou	ciowan jeo	4	泉州	2

Table 3: The Frequency of the name of places in the Volume 2

Order	Name of places	Manchu texts	Frequency	Chinese texts	Frequency
1	Haitan	hai tan	15	海壇	12
4	Xiamen	hiya men	10	廈門	11
5	Taiwan	tai wan	9	臺灣	8
9	Fujian	fugiyan	8	福建	4
3	Kimmen	gin men	7	金門	7
8	Penghu	peng hū	6	彭湖	3
2	Haicheng	hai ceng	4	海澄	4
6	Nan’ao	nan oo	3	南澳	3
7	Pinghai	ping hai	3	平海	3
10	Tongshan	tung šan	3	銅山	3

Comparing to the previous two volumes, Table 4 shows a different result. Besides the Taiwan, Fujian, and Penghu, the rest of frequency is the same. However, it is apparent that frequencies of Taiwan and Fujian in the Manchu language text are more than in Chinese. Although the texts in the Manchu language and Chinese are slightly different, in terms of name of places, most of them are the same in the volume 3.

Table 4: The Frequency of the name of places in the Volume 3

Order	Name of places	Manchu texts	Frequency	Chinese texts	Frequency
1	Taiwan	tai wan	19	臺灣	11
3	Fujian	fugiyan	16	福建	12
2	Penghu	peng hū	8	彭湖	7
4	Kimmen	gin men	3	金門	3
5	Xiamen	hiya men	3	廈門	3
7	Zhejiang	jegiyang	2	浙江	2
8	Pingyang	ping yang	2	平陽	2
6	Haicheng	hai ceng	1	海澄	1
9	Tongshan	tung šan	1	銅山	1
10	Yungxia	yūn siyoo	1	雲霄	1

Since I have compared the frequency of name of places in the first three volumes, it is obviously that the two texts are different. Although the difference is slight, they are different. Therefore, the Manchu language text or Chinese text are not the translated or copy version from the other. Using diagram is an appropriate approach to see how different the two texts are.

Graph 1: The line-graph of the difference in the Volume 1

Graph 2: The line-graph of the difference in the Volume 2

Graph 3: The line-graph of the difference in the Volume 3

As can be seen, Graph 1, Graph 2, and Graph 3 suggest that the two texts are different in the most frequent name of places. In other words, the more frequent places are mentioned in text, the more different they are. When a place where are mentioned only few times in either text, this suggests that this place was only becoming significant at a certain moment or event during this period. For example, in the volume 2, Nan’ao, Pinghai, and Tongshan were mentioned only because a minister listed certain places where should be garrisoned. Besides this suggestion, these places were not important; to be specific, they should not be mentioned because they were not even the territory of the Qing Empire due to the Coastal Exclusion Policy. I will discuss this in the next section. Therefore, once the places were mentioned more frequent in the texts, they were highly different. In other words, I can confidently conclude that the two texts are different in terms of the frequency of name of places, even though they recorded the exactly the same period and event.

3.   A big picture of the geographical phenonmenon

In the previous section, I have left a question that the less frequent name of places should not appear due to the Coastal Exclusion Policy, but why were they still mentioned in two texts? This question might be able to answer when I incorporate the text mining with mapping together. According to the texts, the first sentence of the volume three addresses an important event. In the second month of the twentieth year of Kangxi Reign Period, the Qing Empire decided to repeal the Coastal Exclusion Policy. In other words, the lands in coastal area abolished due to the Policy could be used by people and government. However, this policy in fact was not successful because a lot of people still returned to their hometown before the policy repealed. This was widely known in Fujian but not in other regions.

In other words, the records in the volume 1 and 2 were the events when the Coastal Exclusion Policy was processed. However, the volume 3 was the record after the Coastal Exclusion Policy just repealed. Therefore, I propose to combine the result of the volume 1 and 2 as one fact but keep the volume 3 as an individual fact to discuss the difference between two texts under the historical phenomenon.

As can be seen in Map 1, between the frontier of blue points and seashore, the coastal area was entirely abandoned by the Qing Empire, so the area was in literal not a part of the empire. Therefore, when I mix the result of text mining and the mapping, this might help to understand history well.

Map 1: The Coastal Exclusion Policy

Map 2 is drawn by combining Map 1 and the result of Table 2, but I erase the large unit of place, such Fujian and Dutch, because I could not identify them in the map. As can been seen, the cities mentioned in text were almost beyond the front line, besides one point, which was Youzhou. In other words, from 1679 and 1680, the most frequent discussion about places located on the area where was belonged to neither the Qing nor the Zheng. By using the similar approach, Map 3 shows the result of Table 3 in the map.

Map 2: The frequency of places in the Manchu language in the volume 1 and 2

Map 3: The frequency of places in Chinese in the volume 1 and 2

Combining Map 1, Map 2, and Map 3, we could gain Map 4. It is interesting the difference between the Manchu language and Chinese sources in the map. Since the Manchu language mentioned these areas, where were not a part of the Qing, more direct than in Chinese, this is probably meaningful. Considering the feature and audience of the Manchu language, the Qing government probably did not allow Chinese general public, who could easily access to Chinese but the Manchu language, to understand details of the failure of the Coastal Exclusion Policy. In other words, this difference might imply how the empire control people’s mind and recognition of the true history.

Map 4: The frequency of places in the Manchu language and Chinese in the volume 1 and 2 under the map of the Coastal Exclusion Policy

What was happened and changed when the Coastal Exclusion Policy was repealed? In fact, although the government prohibited people to return these abandoned areas, increasing people still returned where they settled before the policy processed. As a result, the policy was in reality useless. When the policy was repealed in 1681, people could return their original hometowns and lands. According to the previous discussion, if it is true that the reason to mention cities in abandoned area in Chinese less frequent and direct than in the Manchu language is because the government attempted to control people’s understanding, Map 5, Map 6, and Map 7 could exactly interpret why the two texts are similar in the volume 3. Because the Coastal Exclusion Policy had been repealed, it was not necessary to hide from anything about the fail of Coastal Exclusion Policy.

Map 5: The frequency of places in the Manchu language in the volume 3

Map 6: The frequency of places in Chinese in the volume 3

Map 7: The frequency of places in the Manchu language and Chinese under the repealed Coastal Exclusion Policy

Map 8 is mixed Map 1 to 7. It might suggest and support my argument in previous paragraph. Therefore, I can certainly be confident to argue that the Manchu language was more precise, detailed, and direct to mentioned the name of places than in Chinese because the government did not reveal the failure of processing the Coastal Exclusion Policy. Although the failure of the Coastal Exclusion Policy was widely known in Fujian, it was not recognized in other provinces and non-China regions, such as Mongolia and Tibet. Because the main purpose of editing this book is to proclaim the imperial prestige and success, the government had to carefully control the content. The threshold of learning the Manchu language was higher than learning Chinese because the Manchu language was only used in high class. In contrast with the Manchu language, Chinese had been the dominant language for over two thousand years. The failure of the Coastal Exclusion Policy could be limitedly recognized by ruling class, but this could not be known by Chinese folks.

Map 8: The frequency of places in the Manchu language and Chinese under the Coastal Exclusion Policy in the first three volumes

4.   Conclusion

According to the approach of the digitial humanities, conducting text mining to compare two different languages of the same book suggests that the Manchu language or Chinese text was not the copy or translation version of the other. Moreover, the frequency of places in the Manchu language is slightly more precise than Chinese version. Moreover, because the historical background, the frequency of places in this book might be highly related to the imperial policy, the Coastal Exclusion Policy. In fact, combining the text mining and spatial history, it shows how the government controlled texts to limit folks to recognize the failure of the Coastal Exclusion Policy.

Admittedly, I can read the Manchu language and Chinese. Frankly, before I used the approach of the digital humanities to analyze these two texts, I believe that the two texts in fact were exactly the same although I’m a follower of the New Qing History, which means that I did not believe the Manchu language sources were translated from Chinese sources. However, in this case, for me, there was probably a main draft or main author, and the two texts were just edited from the main draft. However, because of the difference between the frequency of places, I change my mind. Also, this enhances the idea of the New Qing History: the Manchu language and Chinese sources should be equally emphasized in order to establish a broader Qing history.