Text Data Preparation: a Practice in R using the Sheng Xuanhuai Collection
3:49 PM
In this post, we share a way of preparing Chinese text data for computational analysis; we do so in R using sample texts from a historical collection that is currently being digitized by our library - the Sheng Xuanhuai Collection.
The Sheng Xuanhuai collection contains over 70 volumes of correspondences between the entrepreneur Sheng Xuanhuai and other individuals. The texts included in the collection are digitized and are preserved in formats of images and text files. The texts are also coded with labels/variables such as title, sender name, receiver name, date, key words and locations mentioned in the texts. The digitization and transcription of the correspondences that transform these texts into machine readable formats allows researchers to conduct studies using computational text analysis and other relevant methods.
In the following sections, we demonstrate our way of importing text data to R, preparing texts for analysis, as well as exploring and visualizing texts. Basic knowledge of R will be helpful if you are to try this practice or even apply this on your own data.
Let’s start fresh by removing what we have loaded and created in R.
Save all the data files in one folder, then plug in the path of the folder in the multcomb function to combine all the data files - here we save the combined data frame as mydata.
We can then generate a data frame indicating frequency of each features using
We can also use
In this blog post, we share our way of preparing Chinese texts for computational text analysis, mainly using two R packages - jiebaR and quanteda. We hope this will help our users either to use our Sheng Xuanhuai correspondences collection or to apply this way of processing Chinese texts on your text data. The two packages provide a lot more functions than what we can introduce in this single post, to learn more about the two text analysis packages, start from their documentations at https://qinwenfeng.com/jiebaR/. and http://docs.quanteda.io/index.html.
The Sheng Xuanhuai collection contains over 70 volumes of correspondences between the entrepreneur Sheng Xuanhuai and other individuals. The texts included in the collection are digitized and are preserved in formats of images and text files. The texts are also coded with labels/variables such as title, sender name, receiver name, date, key words and locations mentioned in the texts. The digitization and transcription of the correspondences that transform these texts into machine readable formats allows researchers to conduct studies using computational text analysis and other relevant methods.
In the following sections, we demonstrate our way of importing text data to R, preparing texts for analysis, as well as exploring and visualizing texts. Basic knowledge of R will be helpful if you are to try this practice or even apply this on your own data.
Import Text
First we need to read our data (the texts - csv files are used in this demo) to R - to do so, we usesetwd() function to set up the working directory, i.e., let R know the path of where we store the data on the computer, then we use read.csv() to load the data file named v36.csv to R.# set your working directory
setwd('YOUR WORKING DIRECTORY')# load the data in a spreadsheet to R
v36 <- read.csv('v36.csv', encoding = 'UTF-8', header=TRUE, row.names=1, stringsAsFactors=FALSE)
# view the first two rows of the data
head(v36, 2)## lid
## 1 36-001A-1—001A-2
## 2 36-001B-1—001B-2
## ltext
## 1 宮保鈞座：新正趨賀兩次，聞藎躬尚未康復，不可以風，未敢驚擾。昨往拜朱邇典。伊面稱久違侍鈞顔，欲於日內晉謁，兼答拜經方。頃接其來函，准於明日下午三句鐘來署拜謁。可否允其接見？謹請示復，以便函告該使。肅此。上叩勛祺，順頌新禧。經方謹上。初九日。
## 2 啟者：頃據民政部公啟稱：於本月十六日下午三點鐘，在部開第一次衛生會會議。請飭派出各員届時蒞會等因。査本部知醫者惟屈道永秋( 去年九月始行札調到部)，及承政廳行走學部主事謝天保兩員。屈道在本部月支薪水二百兩，謝主事則於滬甯鐵路掛名，月支洋百元。此兩員在本部無事，可否派其前往會議？請酌示。經方謹上。十二日。Segmenting Chinese Text
Words and terms are the basic units of many computational text analysis methods, however Chinese characters are not “naturally” divided by whitespaces like some other languages such as English. A number of methods are developed to segment Chinese characters - here we try the widely used “jieba” segmenter on our sample texts. To use the R version of jieba, install the package by running this commandinstall.packages('jiebaR') in you R Console. Note you also need to run install.packages() for the other packages we use here in the following sections if you haven’t had them installed on your computer.# load the "jiebaR" and "stringr" packages/libraries
library(jiebaR)
library(stringr)# initialize jiebaR worker
cutter <- worker()
# test the worker
cutter["今天的天氣真好"]## [1] "今天" "的" "天氣" "真好"# define the function of segmenting
seg_x <- function(x) {str_c(cutter[x], collapse = " ")}
# apply the function to each document (row of ltext)
x.out <- sapply(v36$ltext, seg_x, USE.NAMES = FALSE)
# attach the segmented text back to the data frame
v36$ltext.seg <- x.out
# view the first two rows of the data frame
head(v36, 2)## lid
## 1 36-001A-1—001A-2
## 2 36-001B-1—001B-2
## ltext
## 1 宮保鈞座：新正趨賀兩次，聞藎躬尚未康復，不可以風，未敢驚擾。昨往拜朱邇典。伊面稱久違侍鈞顔，欲於日內晉謁，兼答拜經方。頃接其來函，准於明日下午三句鐘來署拜謁。可否允其接見？謹請示復，以便函告該使。肅此。上叩勛祺，順頌新禧。經方謹上。初九日。
## 2 啟者：頃據民政部公啟稱：於本月十六日下午三點鐘，在部開第一次衛生會會議。請飭派出各員届時蒞會等因。査本部知醫者惟屈道永秋( 去年九月始行札調到部)，及承政廳行走學部主事謝天保兩員。屈道在本部月支薪水二百兩，謝主事則於滬甯鐵路掛名，月支洋百元。此兩員在本部無事，可否派其前往會議？請酌示。經方謹上。十二日。
## ltext.seg
## 1 宮保 鈞座 新 正 趨 賀 兩次 聞 藎 躬 尚未 康復 不 可以 風 未敢 驚擾 昨往 拜 朱邇 典 伊面 稱 久違 侍鈞 顔 欲 於 日 內 晉謁 兼 答拜 經方 頃接 其 來函 准 於 明日 下午 三句 鐘來署 拜謁 可否 允其 接見 謹 請示 復 以便 函告 該 使 肅此 上叩 勛 祺 順頌 新禧 經方謹 上 初九 日
## 2 啟者 頃 據 民政部 公 啟稱 於 本月 十六日 下午 三點鐘 在 部開 第一次 衛生 會 會議 請 飭 派出 各員届 時 蒞會 等 因 査 本部 知 醫者 惟屈 道 永秋 去年 九月 始行 札 調到 部 及承政廳 行走 學部 主事謝 天保 兩員 屈道 在 本部 月 支 薪水 二百兩 謝主事則 於 滬 甯 鐵路 掛名 月 支洋 百元 此 兩員 在 本部 無事 可否 派 其 前往 會議 請 酌 示 經方謹 上 十二日Create corpus and document-term/feature-matrix
With the texts segmented by whitespaces, we can move on to create corpus and document-term/feature-matrix (DTM/DFM) that are often used for further text analysis. Here we use functions of the quanteda package to create corpus and DFMs, so does to explore and visualize the texts. quanteda is an R package for managing and analyzing text data; it provides tools for corpus management, natural language processing, document-feature-matrix analysis and more.# load the library
library(quanteda)corpus() function. We also tokenize the texts using tokens() and construct a document-feature-matrix using dfm(). Note “fasterword” is specified so that the texts are tokenized by whitespaces. We can then view the most frequent terms/features in this set of texts using topfeatures(). The quanteda package also offers a function textplot_wordcloud() by which you can easily plot a wordcloud from DFMs.# create corpus
lcorpus <- corpus(v36$ltext.seg)# summarize the lcorpus object
summary(lcorpus, showmeta = TRUE, 5)## Corpus consisting of 245 documents, showing 5 documents:
##
## Text Types Tokens Sentences
## text1 73 82 1
## text2 82 101 1
## text3 121 153 1
## text4 64 70 1
## text5 171 298 1
##
## Source: /Users/Guest/Desktop/sheng/* on x86_64 by Guest
## Created: Wed Dec 27 15:14:39 2017
## Notes:# see the text in the 1st document of lcorpus
texts(lcorpus)[1]## text1
## "宮保 鈞座 新 正 趨 賀 兩次 聞 藎 躬 尚未 康復 不 可以 風 未敢 驚擾 昨往 拜 朱邇 典 伊面 稱 久違 侍鈞 顔 欲 於 日 內 晉謁 兼 答拜 經方 頃接 其 來函 准 於 明日 下午 三句 鐘來署 拜謁 可否 允其 接見 謹 請示 復 以便 函告 該 使 肅此 上叩 勛 祺 順頌 新禧 經方謹 上 初九 日"# create dfm with "terms/features" spliting by whitespaces;
# ie, preserve what has done for segmenting by jiebaR
# tokenize:"tokens" from doc 1, split by whitespaces
tokens(lcorpus, what = "fasterword")[1]## tokens from 1 document.
## text1 :
## [1] "宮保" "鈞座" "新" "正" "趨" "賀" "兩次"
## [8] "聞" "藎" "躬" "尚未" "康復" "不" "可以"
## [15] "風" "未敢" "驚擾" "昨往" "拜" "朱邇" "典"
## [22] "伊面" "稱" "久違" "侍鈞" "顔" "欲" "於"
## [29] "日" "內" "晉謁" "兼" "答拜" "經方" "頃接"
## [36] "其" "來函" "准" "於" "明日" "下午" "三句"
## [43] "鐘來署" "拜謁" "可否" "允其" "接見" "謹" "請示"
## [50] "復" "以便" "函告" "該" "使" "肅此" "上叩"
## [57] "勛" "祺" "順頌" "新禧" "經方謹" "上" "初九"
## [64] "日"# tokenize and create document-feature-matrix
ltokens <- tokens(v36$ltext.seg, what = "fasterword")
ldfm <- dfm(ltokens)
# a dfm with 245 documents and 8052 features
ldfm ## Document-feature matrix of: 245 documents, 8,052 features (98.9% sparse).# list top 20 features
topfeatures(ldfm, 20)## 上 宮保 鈞座 經方謹 之 肅頌 復 與 已 崇綏
## 216 194 176 171 168 149 147 134 134 130
## 呈 為 又 在 於 請 附 係 以 有
## 122 109 107 105 97 87 86 86 85 85# plot wordcloud
par(family='Kaiti TC') # set Chinese font on Mac; you may not need to set font on Windows
textplot_wordcloud(ldfm, min.freq=30, random.order=FALSE,
colors = RColorBrewer::brewer.pal(8,"Dark2"))Combine multiple data files
In the above lines we show how to work with texts stored in one single file, however it is also fairly common that we have texts saved in multiple files. Here we demonstrate how we combine more than one text file in a more efficient way than do it one by one and also some more ways and options of segmenting, exploring and visualizing text data.Let’s start fresh by removing what we have loaded and created in R.
# remove everything in R environment
rm(list=ls())We first define a function named multcomb to do the following: 1) list the file names of all the data files that you would like to combine to one file - in this case, we have two csv files to combine, 2) read in the files one by one and
rbind them to one data frame.Save all the data files in one folder, then plug in the path of the folder in the multcomb function to combine all the data files - here we save the combined data frame as mydata.
# define the function of combining multiple files
multcomb <- function(mypath){
# save all the file names (with path) in an object "filenames"
filenames <- list.files(path=mypath, full.names=TRUE)
# import all files and save them as "datalist"
datalist <- lapply(filenames, function(x){
read.csv(file=x, encoding='UTF-8', header=TRUE, row.names=1, stringsAsFactors=FALSE)})
# combine the files (data frames in "datalist")
Reduce(function(x,y) {rbind(x,y)}, datalist)}# Use the function multcomb to combine the files in the folder:
# before excecute the function, save all the csv. files in one folder;
# note the folder should not contain other files
mydata <- multcomb('YOUR PATH OF THE FOLDER')# view the first two rows of mydata
head(mydata, 2)## lid
## 1 36-001A-1—001A-2
## 2 36-001B-1—001B-2
## ltext
## 1 宮保鈞座：新正趨賀兩次，聞藎躬尚未康復，不可以風，未敢驚擾。昨往拜朱邇典。伊面稱久違侍鈞顔，欲於日內晉謁，兼答拜經方。頃接其來函，准於明日下午三句鐘來署拜謁。可否允其接見？謹請示復，以便函告該使。肅此。上叩勛祺，順頌新禧。經方謹上。初九日。
## 2 啟者：頃據民政部公啟稱：於本月十六日下午三點鐘，在部開第一次衛生會會議。請飭派出各員届時蒞會等因。査本部知醫者惟屈道永秋( 去年九月始行札調到部)，及承政廳行走學部主事謝天保兩員。屈道在本部月支薪水二百兩，謝主事則於滬甯鐵路掛名，月支洋百元。此兩員在本部無事，可否派其前往會議？請酌示。經方謹上。十二日。Segmenting: stopwords and dictionary
Segment the words in the combined data file - this time we use stopwords and dictionary to modify the “worker” of segmenting.# see the stopwords and dictionary
readLines('sheng_stop.txt', encoding = 'UTF-8')## [1] "" "之" "與" "為" "也" "有" "在" "以" "於" "即" "係"readLines('sheng_dic.txt', encoding = 'UTF-8')## [1] "" "經方" "謹上" "滬甯" "京奉" "匯豐" "匯理"# set up and apply the worker and function for segmenting
cutter <- worker(stop_word = 'sheng_stop.txt', user = 'sheng_dic.txt')
seg_x <- function(x) {str_c(cutter[x], collapse = " ")}
mydata$ltext.seg <- sapply(mydata$ltext, seg_x, USE.NAMES = FALSE)
# view the first few rows
head(mydata, 2)## lid
## 1 36-001A-1—001A-2
## 2 36-001B-1—001B-2
## ltext
## 1 宮保鈞座：新正趨賀兩次，聞藎躬尚未康復，不可以風，未敢驚擾。昨往拜朱邇典。伊面稱久違侍鈞顔，欲於日內晉謁，兼答拜經方。頃接其來函，准於明日下午三句鐘來署拜謁。可否允其接見？謹請示復，以便函告該使。肅此。上叩勛祺，順頌新禧。經方謹上。初九日。
## 2 啟者：頃據民政部公啟稱：於本月十六日下午三點鐘，在部開第一次衛生會會議。請飭派出各員届時蒞會等因。査本部知醫者惟屈道永秋( 去年九月始行札調到部)，及承政廳行走學部主事謝天保兩員。屈道在本部月支薪水二百兩，謝主事則於滬甯鐵路掛名，月支洋百元。此兩員在本部無事，可否派其前往會議？請酌示。經方謹上。十二日。
## ltext.seg
## 1 宮保 鈞座 新 正 趨 賀 兩次 聞 藎 躬 尚未 康復 不 可以 風 未敢 驚擾 昨往 拜 朱邇 典 伊面 稱 久違 侍鈞 顔 欲 日 內 晉謁 兼 答拜 經方 頃接 其 來函 准 明日 下午 三句 鐘來署 拜謁 可否 允其 接見 謹 請示 復 以便 函告 該 使 肅此 上叩 勛 祺 順頌 新禧 經方 謹上 初九 日
## 2 啟者 頃 據 民政部 公 啟稱 本月 十六日 下午 三點鐘 部開 第一次 衛生 會 會議 請 飭 派出 各員届 時 蒞會 等 因 査 本部 知 醫者 惟屈 道 永秋 去年 九月 始行 札 調到 部 及承政廳 行走 學部 主事謝 天保 兩員 屈道 本部 月 支 薪水 二百兩 謝主事則 滬甯 鐵路 掛名 月 支洋 百元 此 兩員 本部 無事 可否 派 其 前往 會議 請 酌 示 經方 謹上 十二日Create corpus, DFM and feature frequency tab
Now we have segmented texts saved in the variable ltext.seg of mydata. We then use functionscorpus() and dfm() to create corpus and DFM from ltext.seg and save them as mycorpus and mydfm.# create and examine corpus
mycorpus <- corpus(mydata$ltext.seg)
summary(mycorpus, showmeta = TRUE, 5)## Corpus consisting of 335 documents, showing 5 documents:
##
## Text Types Tokens Sentences
## text1 73 79 1
## text2 79 95 1
## text3 119 148 1
## text4 62 68 1
## text5 165 277 1
##
## Source: /Users/Guest/Desktop/sheng/* on x86_64 by Guest
## Created: Wed Dec 27 15:14:41 2017
## Notes:# view texts in the first document of the corpus
texts(mycorpus)[1]## text1
## "宮保 鈞座 新 正 趨 賀 兩次 聞 藎 躬 尚未 康復 不 可以 風 未敢 驚擾 昨往 拜 朱邇 典 伊面 稱 久違 侍鈞 顔 欲 日 內 晉謁 兼 答拜 經方 頃接 其 來函 准 明日 下午 三句 鐘來署 拜謁 可否 允其 接見 謹 請示 復 以便 函告 該 使 肅此 上叩 勛 祺 順頌 新禧 經方 謹上 初九 日"# create and examine DFM
mydfm <- dfm(tokens(mydata$ltext.seg, what = "fasterword"))
mydfm ## Document-feature matrix of: 335 documents, 15,126 features (99.2% sparse).# top 20 features
topfeatures(mydfm, 20)## 亦 宮保 已 經方 鈞座 謹上 又 復 稟 不 者 肅頌 呈 均 其
## 271 265 256 219 208 196 195 187 177 161 160 149 145 143 134
## 請 再 崇綏 而 寳
## 131 131 130 125 123We can then generate a data frame indicating frequency of each features using
textstat_frequency().# tabulate feature frequency
dfmtab <- textstat_frequency(mydfm)
head(dfmtab)## feature frequency rank docfreq
## 1 亦 271 1 106
## 2 宮保 265 2 236
## 3 已 256 3 141
## 4 經方 219 4 211
## 5 鈞座 208 5 208
## 6 謹上 196 6 196dfm_select() to choose those meet certain conditions, e.g., terms contain two or more words.# select 2+ word features
mydfm2 <- dfm_select(mydfm, min_nchar = 2)
topfeatures(mydfm2, 20)## 宮保 經方 鈞座 謹上 肅頌 崇綏 不知 合同 公司 不能
## 265 219 208 196 149 130 97 90 85 80
## 閣下 一切 謹悉 外務部 本部 尚未 本日 大人 如何 可以
## 73 59 58 57 54 53 53 53 53 51# plot wordcloud
par(family='Kaiti TC')
textplot_wordcloud(mydfm2, min.freq=5, random.order=FALSE, max.words = 200,
rot.per = .25, scale = c(2.8, .5),
colors = RColorBrewer::brewer.pal(8,"Dark2"))Explore more about the data
The functiontextstat_frequency() can tabulate all feature frequencies like we did above - we can also limit the frequencies to be tabulated and plot these selected features using ggplot() of the ggplot2 package.# tabulate the top 10 features
textstat_frequency(mydfm2, n=10)## feature frequency rank docfreq
## 1 宮保 265 1 236
## 2 經方 219 2 211
## 3 鈞座 208 3 208
## 4 謹上 196 4 196
## 5 肅頌 149 5 149
## 6 崇綏 130 6 130
## 7 不知 97 7 49
## 8 合同 90 8 47
## 9 公司 85 9 45
## 10 不能 80 10 56# plot freq. by rank of the most frequent 50 features
library(ggplot2)
theme_set(theme_minimal())
textstat_frequency(mydfm2, n = 50) %>%
ggplot(aes(x = rank, y = frequency)) +
geom_point() +
labs(x = "Frequency rank", y = "Term frequency")We can also use
dfm_weight to create a DFM representing weighted frequencies of the terms, for instance, a DFM with relative feature/term frequencies, i.e., the proportion of the feature counts of total feature counts.# create dfm with relative term frequencies
dfmpct <- dfm_weight(mydfm2, type = "relfreq")
# plot relative term frequencies
textstat_frequency(dfmpct, n = 10) %>%
ggplot(aes(x = reorder(feature, -rank), y = frequency)) +
geom_bar(stat = "identity") + coord_flip() +
labs(x = "", y = "Relative Term Frequency") +
theme(text = element_text(family = 'STKaiti'))In Sum...
In this blog post, we share our way of preparing Chinese texts for computational text analysis, mainly using two R packages - jiebaR and quanteda. We hope this will help our users either to use our Sheng Xuanhuai correspondences collection or to apply this way of processing Chinese texts on your text data. The two packages provide a lot more functions than what we can introduce in this single post, to learn more about the two text analysis packages, start from their documentations at https://qinwenfeng.com/jiebaR/. and http://docs.quanteda.io/index.html.
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