Example: Chinese text analysis

Haiyan Wang

Source: vignettes/pkgdown/examples/chinese.Rmd

Download corpus

Download corpus constructed from Report on the Work of the Government published by Premier of the State Council between 1954 and 2017. You can download the corpus using the quanteda.corpora package.

# read text files
remotes::install_github("quanteda/quanteda.corpora")
library("quanteda.corpora")
corp <- quanteda.corpora::download(url = "https://www.dropbox.com/s/37ojd5knz1qeyul/data_corpus_chinesegovreport.rds?dl=1")

Tokenization 

# Chinese stopwords
ch_stop <- stopwords("zh", source = "misc")

# tokenize
toks_ch <- corp |> 
    tokens(remove_punct = TRUE) |>
    tokens_remove(pattern = ch_stop)

# Construct a dfm
dfmat_ch <- dfm(toks_ch)

# Get most frequent features
topfeatures(dfmat_ch)
## 发展 经济 社会 建设 改革 人民 主义 工作 企业 国家 
## 5627 5036 4255 4248 2931 2897 2817 2642 2627 2595

Analysis

Word cloud 

# Plot a word cloud
set.seed(100)

# to set the font correctly for macOS
library("quanteda.textplots")
textplot_wordcloud(dfmat_ch, min_count = 500, random_order = FALSE,
                   rotation = .25, max_words = 100,
                   min_size = 0.5, max_size = 2.8,
                   font = if (Sys.info()['sysname'] == "Darwin") "SimHei" else NULL,
                   color = RColorBrewer::brewer.pal(8, "Dark2"))

Feature co-occurrence matrix 

# fcm within the window size of 5
corp_ch17 <- corpus_subset(corp, Year == "2017")

toks_ch17 <- corp_ch17 |> 
    tokens(remove_punct = TRUE) |> 
    tokens_remove(ch_stop)
    
fcmat_ch <- corp_ch17 |> 
    tokens() |> 
    fcm(context = "window", window = 5)

Unsupervised document scaling

In this example, we run a Wordfish model to show how to apply an unspervised document scaling method to Chinese texts.

## Warning: package 'quanteda.textmodels' was built under R version 4.4.1
tmod_wf <- textmodel_wordfish(dfmat_ch)

y <- 1954:2017
y <- y[y <= 1964 | y >= 1975]
y <- y[!y %in% c(1963, 1961, 1962, 1976, 1977)]
plot(y, tmod_wf$theta, xlab = "Year", ylab = "Position")

Collocations 

# bigrams cross the whole dataset
library("quanteda.textstats")

tstat_col_ch <- textstat_collocations(toks_ch, size = 2, min_count = 20)
knitr::kable(head(tstat_col_ch, 10))
collocation count count_nested length lambda z
社会 主义 1787 0 2 5.667646 128.76439
亿 元 689 0 2 7.451647 93.09479
现代 化 632 0 2 6.956866 83.62882
体制 改革 504 0 2 5.199483 77.47483
五年 计划 341 0 2 5.365465 71.73289
各级 政府 306 0 2 6.116987 66.70430
增长 百分 300 0 2 5.527159 65.95692
万 吨 212 0 2 6.596337 62.62405
国民 经济 589 0 2 6.021117 61.87053
充分 发挥 191 0 2 6.590507 61.30822 
# bigrams in 2017 report
tstat_col_ch17 <- textstat_collocations(toks_ch17, size = 2)
knitr::kable(head(tstat_col_ch17, 10))
collocation count count_nested length lambda z
人民 群众 12 0 2 5.406843 12.89491
亿 元 14 0 2 8.302839 12.62184
调 控 11 0 2 7.593829 12.41301
政府 工作 9 0 2 4.710228 11.07990
深入 实施 8 0 2 5.018592 10.92455
党 中央 7 0 2 5.747235 10.90905
体制 改革 11 0 2 5.317394 10.53589
国内 生产 6 0 2 6.166877 10.48876
现代 化 8 0 2 5.706046 10.43500
基础 设施 7 0 2 7.549629 10.42514