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带有TD和Quanteda西班牙语字符的R西班牙语术语频率矩阵

quanteda encode special-characters r

Beep · 技术社区 · 7 年前

我正在尝试学习如何使用推特数据进行文本分析。我在创建术语频率矩阵时遇到了一个问题。我用西班牙语文本(带有特殊字符)创建语料库,没有任何问题。

但是,当我创建术语频率矩阵(使用quanteda或tm库)时,西班牙语字符不会按预期显示(我看到的不是cancin,而是cancin)。

关于如何获得术语频率矩阵来存储具有正确字符的文本,有什么建议吗?

谢谢你的帮助。

请注意:我更喜欢使用quanteda库,因为最终我将创建一个wordcloud,我想我更了解这个库的方法。我也在使用Windows计算机。

我尝试过编码(tw2)<-“UTF-8”没有运气。

library(dplyr)
library(tm)
library(quanteda)

#' Creating a character with special Spanish characters:
tw2 <- "RT @None: Enmascarados, si masduro chingÃ¡n a tarek. Si quieres ahora, la aguantas canciÃ³n  . https://t."


#Cleaning the tweet, removing special punctuation, numbers http links, 
extra spaces:
clean_tw2 <- tolower(tw2)
clean_tw2 = gsub("&amp", "", clean_tw2)
clean_tw2 = gsub("(rt|via)((?:\\b\\W*@\\w+)+)", "", clean_tw2)
clean_tw2 = gsub("@\\w+", "", clean_tw2)
clean_tw2 = gsub("[[:punct:]]", "", clean_tw2)
clean_tw2 = gsub("http\\w+", "", clean_tw2)
clean_tw2 = gsub("[ \t]{2,}", "", clean_tw2)
clean_tw2 = gsub("^\\s+|\\s+$", "", clean_tw2) 

# creates a vector with common stopwords, and other words which I want removed.
myStopwords <- c(stopwords("spanish"),"tarek","vez","ser","ahora")
clean_tw2 <- (removeWords(clean_tw2,myStopwords))

# If we print clean_tw2 we see that all the characters are displayed as expected.
clean_tw2

#'Create Corpus Using quanteda library
corp_quan<-corpus(clean_tw2)
# The corpus created via quanteda, displays the characters as expected.
corp_quan$documents$texts

#'Create Corpus Using TD library
corp_td<-Corpus(VectorSource(clean_tw2))
#' Remove common words from spanish from the Corpus.
#' If we inspect the corp_td, we see that the characters and words are displayed correctly
inspect(corp_td)

# Create the DFM with quanteda library.
tdm_quan<-dfm(corp_quan)
# Here we see that the spanish characters are displayed incorrectly for Example: canciÃ³n = canciÃÂ³n
tdm_quan

# Create the TDM with TD library
tdm_td<-TermDocumentMatrix(corp_td)

# Here we see that the Spanish characters are displayed incorrectly (e.g. canciÃ³n = canciÃ), and "si" is missing.
tdm_td$dimnames$Terms

2 回复 | 直到 7 年前

phiver 7 年前

在windows平台上创建DFM时,quanteda(和tm)似乎正在丢失编码。在里面 this tidytext 问题同样的问题也会发生在未测试的令牌上。现在也很好用 quanteda 的 tokens 工作正常。如果我强制执行 UTF-8 或 latin1 在上编码 @Dimnames$features 的 dfm 您得到了正确的结果。

....
previous code
.....
tdm_quan<-dfm(corp_quan)
# Here we see that the spanish characters are displayed incorrectly for Example: canciÃ³n = canciÃÂ³n
tdm_quan
Document-feature matrix of: 1 document, 8 features (0% sparse).
1 x 8 sparse Matrix of class "dfm"
       features
docs    enmascarados si masduro chingÃÂ¡n quieres aguantas canciÃÂ³n t
  text1            1  2       1        1       1        1        1 1

如果您执行以下操作:

Encoding(tdm_quan@Dimnames$features) <- "UTF-8"
tdm_quan
Document-feature matrix of: 1 document, 8 features (0% sparse).
1 x 8 sparse Matrix of class "dfm"
       features
docs    enmascarados si masduro chingÃ¡n quieres aguantas canciÃ³n t
  text1            1  2       1       1       1        1       1 1

Ken Benoit 7 年前

让我猜猜。。。您正在使用Windows吗?在macOS上工作正常:

clean_tw2
## [1] "enmascarados si masduro chingÃ¡n   si quieres   aguantas canciÃ³n"
Encoding(clean_tw2)
## [1] "UTF-8"
dfm(clean_tw2)
## Document-feature matrix of: 1 document, 7 features (0% sparse).
## 1 x 7 sparse Matrix of class "dfm"
##        features
## docs    enmascarados si masduro chingÃ¡n quieres aguantas canciÃ³n
##   text1            1  2       1       1       1        1       1

我的系统信息:

sessionInfo()
# R version 3.4.4 (2018-03-15)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.4
# 
# Matrix products: default
# BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
# 
# locale:
# [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] tm_0.7-3       NLP_0.1-11     dplyr_0.7.4    quanteda_1.1.6