この記事では,quantedaの特徴と機能の概要を説明します.より詳しい説明は,quanteda.ioにある記事を参照してください.
quantedaはCRANからインストールできます.GUIのRパッケージインストーラを使用してインストールするか,次のコマンドを実行します.
install.packages("quanteda")
GitHubから最新の開発バージョンをインストールする方法については,https://github.com/kbenoit/quanteda を参照してください.
quantedaには連携して機能を拡張する一連のパッケージがあり,それらをインストールすることをお薦めします.
quantedaData:quantedaの本記事内の説明で使用する追加のテキストデータ
devtools::install_github("kbenoit/quantedaData")
LIWCalike: Linguistic Inquiry and Word Count (LIWC) アプローチによるテキスト分析のR実装
devtools::install_github("kbenoit/LIWCalike")
まず,quantedaを読み込んで,パッケージの関数とデータにアクセスできるようにします.
library(quanteda)
quantedaにはテキストを読み込むためのシンプルで強力なパッケージ,readtextがあります.このパッケージのreadtext()
は,ローカス・ストレージやインターネットからファイル読み込み,corpus()
にデータ・フレームを返します.
readtext()
で利用可能なファイルやデータの形式:
.txt
)ファイル.csv
)ファイルquantedaのコーパスを生成する関数である corpus()
は,以下の種類のデータを読み込むことができます.
VCorpus
コーパスオブジェクトコーパスを作成する最も簡単な方法は,corpus()
を用いて,すでにRに読み込まれた文字列ベクトル作成することです.文字列ベクトルをRに取り込む方法はさまざまなので,高度なRユーザーは,コーパスをいろいろな方法で作り出せます.
次の例では,quantedaパッケージに含まれているイギリスの政党が2010年の総選挙のために発行したマニフェストのテキストデータ(data_char_ukimmig2010
)からコーパスを作成しています.
myCorpus <- corpus(data_char_ukimmig2010) # テキストからコーパスを作成
summary(myCorpus)
## Corpus consisting of 9 documents:
##
## Text Types Tokens Sentences
## BNP 1125 3280 88
## Coalition 142 260 4
## Conservative 251 499 15
## Greens 322 679 21
## Labour 298 683 29
## LibDem 251 483 14
## PC 77 114 5
## SNP 88 134 4
## UKIP 346 723 27
##
## Source: /home/kohei/packages/quanteda/docs/articles/pkgdown/* on x86_64 by kohei
## Created: Fri Dec 15 11:54:28 2017
## Notes:
コーパスを作成したあとでも,docvars
を用いると,必要に応じて文書に対応した変数をこのコーパスに追加することができます.
たとえば,Rのnames()
関数を使って文字ベクトル(data_char_ukimmig2010
)の名前を取得し,これを文書変数(docvar()
)に追加することができます.
docvars(myCorpus, "Party") <- names(data_char_ukimmig2010)
docvars(myCorpus, "Year") <- 2010
summary(myCorpus)
## Corpus consisting of 9 documents:
##
## Text Types Tokens Sentences Party Year
## BNP 1125 3280 88 BNP 2010
## Coalition 142 260 4 Coalition 2010
## Conservative 251 499 15 Conservative 2010
## Greens 322 679 21 Greens 2010
## Labour 298 683 29 Labour 2010
## LibDem 251 483 14 LibDem 2010
## PC 77 114 5 PC 2010
## SNP 88 134 4 SNP 2010
## UKIP 346 723 27 UKIP 2010
##
## Source: /home/kohei/packages/quanteda/docs/articles/pkgdown/* on x86_64 by kohei
## Created: Fri Dec 15 11:54:28 2017
## Notes:
分析の対象となる文書変数ではではないけれども,文書の属性として残しておきたいと思うメタデータも,docvars()
を使って,コーパスに追加することができます.
metadoc(myCorpus, "language") <- "english"
metadoc(myCorpus, "docsource") <- paste("data_char_ukimmig2010", 1:ndoc(myCorpus), sep = "_")
summary(myCorpus, showmeta = TRUE)
## Corpus consisting of 9 documents:
##
## Text Types Tokens Sentences Party Year _language
## BNP 1125 3280 88 BNP 2010 english
## Coalition 142 260 4 Coalition 2010 english
## Conservative 251 499 15 Conservative 2010 english
## Greens 322 679 21 Greens 2010 english
## Labour 298 683 29 Labour 2010 english
## LibDem 251 483 14 LibDem 2010 english
## PC 77 114 5 PC 2010 english
## SNP 88 134 4 SNP 2010 english
## UKIP 346 723 27 UKIP 2010 english
## _docsource
## data_char_ukimmig2010_1
## data_char_ukimmig2010_2
## data_char_ukimmig2010_3
## data_char_ukimmig2010_4
## data_char_ukimmig2010_5
## data_char_ukimmig2010_6
## data_char_ukimmig2010_7
## data_char_ukimmig2010_8
## data_char_ukimmig2010_9
##
## Source: /home/kohei/packages/quanteda/docs/articles/pkgdown/* on x86_64 by kohei
## Created: Fri Dec 15 11:54:28 2017
## Notes:
metadoc()
を用いると,文書メタデータのフィールドを自由に定義することができますが,単一の値(“english”)を文書変数(“language”)に付与するときには,Rが値を繰り替えして全ての文書に同じ値を付与していることに注意してください.
独自の文書メタデータのフィールド(docsource
)を作成するために,quantedaの関数であるndoc()
を使ってコーパスに含まれる文書の総数を取得しています.ndoc()
は,nrow()
やncol()
などのRの標準の関数と同じような方法で動作するように設計されています.
require(readtext)
# Twitter json
mytf1 <- readtext("~/Dropbox/QUANTESS/social media/zombies/tweets.json")
myCorpusTwitter <- corpus(mytf1)
summary(myCorpusTwitter, 5)
# generic json - needs a textfield specifier
mytf2 <- readtext("~/Dropbox/QUANTESS/Manuscripts/collocations/Corpora/sotu/sotu.json",
textfield = "text")
summary(corpus(mytf2), 5)
# text file
mytf3 <- readtext("~/Dropbox/QUANTESS/corpora/project_gutenberg/pg2701.txt", cache = FALSE)
summary(corpus(mytf3), 5)
# multiple text files
mytf4 <- readtext("~/Dropbox/QUANTESS/corpora/inaugural/*.txt", cache = FALSE)
summary(corpus(mytf4), 5)
# multiple text files with docvars from filenames
mytf5 <- readtext("~/Dropbox/QUANTESS/corpora/inaugural/*.txt",
docvarsfrom = "filenames", sep = "-", docvarnames = c("Year", "President"))
summary(corpus(mytf5), 5)
# XML data
mytf6 <- readtext("~/Dropbox/QUANTESS/quanteda_working_files/xmlData/plant_catalog.xml",
textfield = "COMMON")
summary(corpus(mytf6), 5)
# csv file
write.csv(data.frame(inaugSpeech = texts(data_corpus_inaugural),
docvars(data_corpus_inaugural)),
file = "/tmp/inaug_texts.csv", row.names = FALSE)
mytf7 <- readtext("/tmp/inaug_texts.csv", textfield = "inaugSpeech")
summary(corpus(mytf7), 5)
quantedaのコーパスは,元の文書をユニコード(UTF-8)に変換し,文書に対するメタデータと一緒に格納しすることで、ステミングや句読点の削除などの処理よって変更されないテキストデータの静的な保管庫になるように設計されています.これによって,コーパスから文書を抽出して新しいオブジェクトを作成した後でも,コーパスには元のデータが残り,別の分析を,同じコーパスを用いて行うことができます.
コーパスから文書を取り出すためには,texts()
と呼ばれる関数を使用します.
texts(data_corpus_inaugural)[2]
## 1793-Washington
## "Fellow citizens, I am again called upon by the voice of my country to execute the functions of its Chief Magistrate. When the occasion proper for it shall arrive, I shall endeavor to express the high sense I entertain of this distinguished honor, and of the confidence which has been reposed in me by the people of united America.\n\nPrevious to the execution of any official act of the President the Constitution requires an oath of office. This oath I am now about to take, and in your presence: That if it shall be found during my administration of the Government I have in any instance violated willingly or knowingly the injunctions thereof, I may (besides incurring constitutional punishment) be subject to the upbraidings of all who are now witnesses of the present solemn ceremony.\n\n "
summary()
により,コーパス内のテキストの要約を行うことができます.
summary(data_corpus_irishbudget2010)
## Corpus consisting of 14 documents:
##
## Text Types Tokens Sentences year debate
## 2010_BUDGET_01_Brian_Lenihan_FF 1953 8641 374 2010 BUDGET
## 2010_BUDGET_02_Richard_Bruton_FG 1040 4446 217 2010 BUDGET
## 2010_BUDGET_03_Joan_Burton_LAB 1624 6393 307 2010 BUDGET
## 2010_BUDGET_04_Arthur_Morgan_SF 1595 7107 343 2010 BUDGET
## 2010_BUDGET_05_Brian_Cowen_FF 1629 6599 250 2010 BUDGET
## 2010_BUDGET_06_Enda_Kenny_FG 1148 4232 153 2010 BUDGET
## 2010_BUDGET_07_Kieran_ODonnell_FG 678 2297 133 2010 BUDGET
## 2010_BUDGET_08_Eamon_Gilmore_LAB 1181 4177 201 2010 BUDGET
## 2010_BUDGET_09_Michael_Higgins_LAB 488 1286 44 2010 BUDGET
## 2010_BUDGET_10_Ruairi_Quinn_LAB 439 1284 59 2010 BUDGET
## 2010_BUDGET_11_John_Gormley_Green 401 1030 49 2010 BUDGET
## 2010_BUDGET_12_Eamon_Ryan_Green 510 1643 90 2010 BUDGET
## 2010_BUDGET_13_Ciaran_Cuffe_Green 442 1240 45 2010 BUDGET
## 2010_BUDGET_14_Caoimhghin_OCaolain_SF 1188 4044 176 2010 BUDGET
## number foren name party
## 01 Brian Lenihan FF
## 02 Richard Bruton FG
## 03 Joan Burton LAB
## 04 Arthur Morgan SF
## 05 Brian Cowen FF
## 06 Enda Kenny FG
## 07 Kieran ODonnell FG
## 08 Eamon Gilmore LAB
## 09 Michael Higgins LAB
## 10 Ruairi Quinn LAB
## 11 John Gormley Green
## 12 Eamon Ryan Green
## 13 Ciaran Cuffe Green
## 14 Caoimhghin OCaolain SF
##
## Source: /Users/kbenoit/Dropbox (Personal)/GitHub/quanteda/* on x86_64 by kbenoit
## Created: Wed Jun 28 22:04:18 2017
## Notes:
summary()
の出力をデータ・フレームとして保存し,基本的な記述統計を描画することができます.
tokenInfo <- summary(data_corpus_inaugural)
if (require(ggplot2))
ggplot(data=tokenInfo, aes(x = Year, y = Tokens, group = 1)) + geom_line() + geom_point() +
scale_x_continuous(labels = c(seq(1789,2012,12)), breaks = seq(1789,2012,12) )
## Loading required package: ggplot2
# Longest inaugural address: William Henry Harrison
tokenInfo[which.max(tokenInfo$Tokens), ]
## Corpus consisting of 58 documents:
##
## Text Types Tokens Sentences Year President FirstName
## 1841-Harrison 1896 9144 210 1841 Harrison William Henry
##
## Source: Gerhard Peters and John T. Woolley. The American Presidency Project.
## Created: Tue Jun 13 14:51:47 2017
## Notes: http://www.presidency.ucsb.edu/inaugurals.php
+
演算子を用いると,簡単に二個のコーパスを連結できます.コーパスが異なる構造を持つ場合でも,文書変数が失われることはなく,コーパスのメタデータも引き継がれます.
library(quanteda)
mycorpus1 <- corpus(data_corpus_inaugural[1:5])
mycorpus2 <- corpus(data_corpus_inaugural[53:58])
mycorpus3 <- mycorpus1 + mycorpus2
summary(mycorpus3)
## Corpus consisting of 11 documents:
##
## Text Types Tokens Sentences
## 1789-Washington 625 1538 23
## 1793-Washington 96 147 4
## 1797-Adams 826 2578 37
## 1801-Jefferson 717 1927 41
## 1805-Jefferson 804 2381 45
## 1997-Clinton 773 2449 111
## 2001-Bush 621 1808 97
## 2005-Bush 773 2319 100
## 2009-Obama 938 2711 110
## 2013-Obama 814 2317 88
## 2017-Trump 582 1660 88
##
## Source: Combination of corpuses mycorpus1 and mycorpus2
## Created: Fri Dec 15 11:54:30 2017
## Notes:
corpus_subset()
により,文書変数に適用される論理条件に基づいて文書を抽出することができます.
summary(corpus_subset(data_corpus_inaugural, Year > 1990))
## Corpus consisting of 7 documents:
##
## Text Types Tokens Sentences Year President FirstName
## 1993-Clinton 642 1833 81 1993 Clinton Bill
## 1997-Clinton 773 2449 111 1997 Clinton Bill
## 2001-Bush 621 1808 97 2001 Bush George W.
## 2005-Bush 773 2319 100 2005 Bush George W.
## 2009-Obama 938 2711 110 2009 Obama Barack
## 2013-Obama 814 2317 88 2013 Obama Barack
## 2017-Trump 582 1660 88 2017 Trump Donald J.
##
## Source: Gerhard Peters and John T. Woolley. The American Presidency Project.
## Created: Tue Jun 13 14:51:47 2017
## Notes: http://www.presidency.ucsb.edu/inaugurals.php
summary(corpus_subset(data_corpus_inaugural, President == "Adams"))
## Corpus consisting of 2 documents:
##
## Text Types Tokens Sentences Year President FirstName
## 1797-Adams 826 2578 37 1797 Adams John
## 1825-Adams 1003 3152 74 1825 Adams John Quincy
##
## Source: Gerhard Peters and John T. Woolley. The American Presidency Project.
## Created: Tue Jun 13 14:51:47 2017
## Notes: http://www.presidency.ucsb.edu/inaugurals.php
kwic()
(keywords-in-context)は単語の検索を行い,その単語が現れる文脈を表示します.
kwic(data_corpus_inaugural, "terror")
##
## [1797-Adams, 1325] fraud or violence, by | terror |
## [1933-Roosevelt, 112] nameless, unreasoning, unjustified | terror |
## [1941-Roosevelt, 287] seemed frozen by a fatalistic | terror |
## [1961-Kennedy, 866] alter that uncertain balance of | terror |
## [1981-Reagan, 813] freeing all Americans from the | terror |
## [1997-Clinton, 1055] They fuel the fanaticism of | terror |
## [1997-Clinton, 1655] maintain a strong defense against | terror |
## [2009-Obama, 1632] advance their aims by inducing | terror |
##
## , intrigue, or venality
## which paralyzes needed efforts to
## , we proved that this
## that stays the hand of
## of runaway living costs.
## . And they torment the
## and destruction. Our children
## and slaughtering innocents, we
kwic(data_corpus_inaugural, "terror", valuetype = "regex")
##
## [1797-Adams, 1325] fraud or violence, by | terror
## [1933-Roosevelt, 112] nameless, unreasoning, unjustified | terror
## [1941-Roosevelt, 287] seemed frozen by a fatalistic | terror
## [1961-Kennedy, 866] alter that uncertain balance of | terror
## [1961-Kennedy, 990] of science instead of its | terrors
## [1981-Reagan, 813] freeing all Americans from the | terror
## [1981-Reagan, 2196] understood by those who practice | terrorism
## [1997-Clinton, 1055] They fuel the fanaticism of | terror
## [1997-Clinton, 1655] maintain a strong defense against | terror
## [2009-Obama, 1632] advance their aims by inducing | terror
## [2017-Trump, 1117] civilized world against radical Islamic | terrorism
##
## | , intrigue, or venality
## | which paralyzes needed efforts to
## | , we proved that this
## | that stays the hand of
## | . Together let us explore
## | of runaway living costs.
## | and prey upon their neighbors
## | . And they torment the
## | and destruction. Our children
## | and slaughtering innocents, we
## | , which we will eradicate
kwic(data_corpus_inaugural, "communist*")
##
## [1949-Truman, 834] the actions resulting from the | Communist |
## [1961-Kennedy, 519] -- not because the | Communists |
##
## philosophy are a threat to
## may be doing it,
上記の要約では,“Year”と“President”は各文書に結び付けられた変数です. docvars()
でこのような変数にアクセスできます.
# inspect the document-level variables
head(docvars(data_corpus_inaugural))
## Year President FirstName
## 1789-Washington 1789 Washington George
## 1793-Washington 1793 Washington George
## 1797-Adams 1797 Adams John
## 1801-Jefferson 1801 Jefferson Thomas
## 1805-Jefferson 1805 Jefferson Thomas
## 1809-Madison 1809 Madison James
# inspect the corpus-level metadata
metacorpus(data_corpus_inaugural)
## $source
## [1] "Gerhard Peters and John T. Woolley. The American Presidency Project."
##
## $notes
## [1] "http://www.presidency.ucsb.edu/inaugurals.php"
##
## $created
## [1] "Tue Jun 13 14:51:47 2017"
quantedaDataをインストールすることで,より多くのコーパスを試すことができます.
文書のスケーリングなどの統計分析を行うためには,それぞれの文書の特長をまとめた行列を作成する必要があります. quantedaでは,このような行列を生成するために dfm()
を使います. dfmはdocument-feature matrixの略で,行が文書(document),列が特長(feature)となる行列です.行と列をこのように定義する理由は,データ分析では行が分析単位になり,各列が分析対象になる変数となるのが一般的だからです.多くのソフトウェアでは,この行列をdocument-term matrixと呼びます.quantedaが語(term)でなはく特長(feature)という用語を使うのは,特長のほうが一般性を持つ用語だからです.テキスト分析では,単語,語幹,単語の集合,Nグラム,品詞など様々なものが文書の特長となります.
テキストを簡単にトークン化するために,quantedaは tokens()
と呼ばれる強力なコマンドを提供します.この関数は,文字ベクトルのトークンのリストからなる中間オブジェクトを生成します.ここで,リストの一つ一つの要素は入力された文書に対応しています.
tokens()
は意図して保守的に設計されており、ユーザーが明示的に指示を与えないかぎりは,要素を削除しません.
txt <- c(text1 = "This is $10 in 999 different ways,\n up and down; left and right!",
text2 = "@kenbenoit working: on #quanteda 2day\t4ever, http://textasdata.com?page=123.")
tokens(txt)
## tokens from 2 documents.
## text1 :
## [1] "This" "is" "$" "10" "in"
## [6] "999" "different" "ways" "," "up"
## [11] "and" "down" ";" "left" "and"
## [16] "right" "!"
##
## text2 :
## [1] "@kenbenoit" "working" ":" "on"
## [5] "#quanteda" "2day" "4ever" ","
## [9] "http" ":" "/" "/"
## [13] "textasdata.com" "?" "page" "="
## [17] "123" "."
tokens(txt, remove_numbers = TRUE, remove_punct = TRUE)
## tokens from 2 documents.
## text1 :
## [1] "This" "is" "in" "different" "ways"
## [6] "up" "and" "down" "left" "and"
## [11] "right"
##
## text2 :
## [1] "@kenbenoit" "working" "on" "#quanteda"
## [5] "2day" "4ever" "http" "textasdata.com"
## [9] "page"
tokens(txt, remove_numbers = FALSE, remove_punct = TRUE)
## tokens from 2 documents.
## text1 :
## [1] "This" "is" "10" "in" "999"
## [6] "different" "ways" "up" "and" "down"
## [11] "left" "and" "right"
##
## text2 :
## [1] "@kenbenoit" "working" "on" "#quanteda"
## [5] "2day" "4ever" "http" "textasdata.com"
## [9] "page" "123"
tokens(txt, remove_numbers = TRUE, remove_punct = FALSE)
## tokens from 2 documents.
## text1 :
## [1] "This" "is" "$" "in" "different"
## [6] "ways" "," "up" "and" "down"
## [11] ";" "left" "and" "right" "!"
##
## text2 :
## [1] "@kenbenoit" "working" ":" "on"
## [5] "#quanteda" "2day" "4ever" ","
## [9] "http" ":" "/" "/"
## [13] "textasdata.com" "?" "page" "="
## [17] "."
tokens(txt, remove_numbers = FALSE, remove_punct = FALSE)
## tokens from 2 documents.
## text1 :
## [1] "This" "is" "$" "10" "in"
## [6] "999" "different" "ways" "," "up"
## [11] "and" "down" ";" "left" "and"
## [16] "right" "!"
##
## text2 :
## [1] "@kenbenoit" "working" ":" "on"
## [5] "#quanteda" "2day" "4ever" ","
## [9] "http" ":" "/" "/"
## [13] "textasdata.com" "?" "page" "="
## [17] "123" "."
tokens(txt, remove_numbers = FALSE, remove_punct = FALSE, remove_separators = FALSE)
## tokens from 2 documents.
## text1 :
## [1] "This" " " "is" " " "$"
## [6] "10" " " "in" " " "999"
## [11] " " "different" " " "ways" ","
## [16] "\n" " " "up" " " "and"
## [21] " " "down" ";" " " "left"
## [26] " " "and" " " "right" "!"
##
## text2 :
## [1] "@kenbenoit" " " "working" ":"
## [5] " " "on" " " "#quanteda"
## [9] " " "2day" "\t" "4ever"
## [13] "," " " "http" ":"
## [17] "/" "/" "textasdata.com" "?"
## [21] "page" "=" "123" "."
tokens
には個々の文字をトークン化するオプションもあります.
tokens("Great website: http://textasdata.com?page=123.", what = "character")
## tokens from 1 document.
## text1 :
## [1] "G" "r" "e" "a" "t" "w" "e" "b" "s" "i" "t" "e" ":" "h" "t" "t" "p"
## [18] ":" "/" "/" "t" "e" "x" "t" "a" "s" "d" "a" "t" "a" "." "c" "o" "m"
## [35] "?" "p" "a" "g" "e" "=" "1" "2" "3" "."
tokens("Great website: http://textasdata.com?page=123.", what = "character",
remove_separators = FALSE)
## tokens from 1 document.
## text1 :
## [1] "G" "r" "e" "a" "t" " " "w" "e" "b" "s" "i" "t" "e" ":" " " "h" "t"
## [18] "t" "p" ":" "/" "/" "t" "e" "x" "t" "a" "s" "d" "a" "t" "a" "." "c"
## [35] "o" "m" "?" "p" "a" "g" "e" "=" "1" "2" "3" "."
もしくは,一文ごとにトークン化するオプションもあります.
# sentence level
tokens(c("Kurt Vongeut said; only assholes use semi-colons.",
"Today is Thursday in Canberra: It is yesterday in London.",
"En el caso de que no puedas ir con ellos, ¿quieres ir con nosotros?"),
what = "sentence")
## tokens from 3 documents.
## text1 :
## [1] "Kurt Vongeut said; only assholes use semi-colons."
##
## text2 :
## [1] "Today is Thursday in Canberra: It is yesterday in London."
##
## text3 :
## [1] "En el caso de que no puedas ir con ellos, ¿quieres ir con nosotros?"
文書をトークン化は中間的な処理であり,ほとんどのユーザーはこれを省いて、すぐに文書行列を作成したいと考えるでしょう. このために,quantedaは,自動的にトークン化を実行し,文書行列を作成するdfm()
と呼ばれるスイスアーミーナイフのように便利な関数を持っています.保守的なtokens()
とは異なり,dfm()
はデフォルトで大文字から小文字への置換や,句読点を除去などの操作を適用します.また,dfm()
からtokens()
の全てのオプションを利用できます.
myCorpus <- corpus_subset(data_corpus_inaugural, Year > 1990)
# make a dfm
myDfm <- dfm(myCorpus)
myDfm[, 1:5]
## Document-feature matrix of: 7 documents, 5 features (0% sparse).
## 7 x 5 sparse Matrix of class "dfm"
## features
## docs my fellow citizens , today
## 1993-Clinton 7 5 2 139 10
## 1997-Clinton 6 7 7 131 5
## 2001-Bush 3 1 9 110 2
## 2005-Bush 2 3 6 120 3
## 2009-Obama 2 1 1 130 6
## 2013-Obama 3 3 6 99 4
## 2017-Trump 1 1 4 96 4
dfm()
の追加のオプションには,ストップワードの削除(remove
)や語のステミング(stem
)が含まれます.
# make a dfm, removing stopwords and applying stemming
myStemMat <- dfm(myCorpus, remove = stopwords("english"),
stem = TRUE, remove_punct = TRUE)
myStemMat[, 1:5]
## Document-feature matrix of: 7 documents, 5 features (17.1% sparse).
## 7 x 5 sparse Matrix of class "dfm"
## features
## docs fellow citizen today celebr mysteri
## 1993-Clinton 5 2 10 4 1
## 1997-Clinton 7 8 6 1 0
## 2001-Bush 1 10 2 0 0
## 2005-Bush 3 7 3 2 0
## 2009-Obama 1 1 6 2 0
## 2013-Obama 3 8 6 1 0
## 2017-Trump 1 4 5 3 1
remove
によっては,文書行列から除外するトークンを指定します.stopwords()
は,幾つかの言語で定義されたストップワードのリストを返します(残念ながら日本語は含まれていませんが,ユーザーが定義した辞書を使うことはできます).
head(stopwords("english"), 20)
## [1] "i" "me" "my" "myself" "we"
## [6] "our" "ours" "ourselves" "you" "your"
## [11] "yours" "yourself" "yourselves" "he" "him"
## [16] "his" "himself" "she" "her" "hers"
head(stopwords("russian"), 10)
## [1] "и" "в" "во" "не" "что" "он" "на" "я" "с" "со"
head(stopwords("arabic"), 10)
## [1] "فى" "في" "كل" "لم" "لن" "له" "من" "هو" "هي" "قوة"
RStudioの“Environment”パネル,またはRのView()
を用いることで,dfmに格納された値を見ることができます.dfmに対してplot()
を用いると,wordcloudを使ってワードクラウドが表示されます.
## Document-feature matrix of: 9 documents, 1,547 features (83.8% sparse).
頻度が最も高い特長を見るには,topfeatures()
を使います.
topfeatures(mydfm, 20) # 20 top words
## immigration british people asylum britain uk
## 66 37 35 29 28 27
## system population country new immigrants ensure
## 27 21 20 19 17 17
## shall citizenship social national bnp illegal
## 17 16 14 14 13 13
## work percent
## 13 12
dfmをtextplot_wordcloud()
に渡すことで,ワードクラウドを描画できます.この関数は,オブジェクトや引数をwordcloudパッケージのwordcloud()
に送るので,ワードクラウドの表示を変更することもできます.
set.seed(100)
textplot_wordcloud(mydfm, min.freq = 6, random.order = FALSE,
rot.per = .25,
colors = RColorBrewer::brewer.pal(8,"Dark2"))
quantedaでは,dfmを作成する際に,文書変数の値によって文書をグループ化するすることができます.
byPartyDfm <- dfm(data_corpus_irishbudget2010, groups = "party",
remove = stopwords("english"), remove_punct = TRUE)
また、以下のように,dfmを語の頻度順に並べ替えて,中身を確かめられます.
dfm_sort(byPartyDfm)[, 1:10]
## Document-feature matrix of: 5 documents, 10 features (0% sparse).
## 5 x 10 sparse Matrix of class "dfm"
## features
## docs people budget government public minister tax economy pay jobs
## FF 23 44 47 65 11 60 37 41 41
## FG 78 71 61 47 62 11 20 29 17
## LAB 69 66 36 32 54 47 37 24 20
## SF 81 53 73 31 39 34 50 24 27
## Green 15 26 19 4 4 11 16 4 15
## features
## docs billion
## FF 32
## FG 21
## LAB 34
## SF 29
## Green 3
肯定的な映画のレビューや,特定の政治意識に関する語があらかじめ分かっている場合,語のグループをまとめて一つのものとして取り扱い,これらを合計して単一の特長として分析すると上手くいくかもしれません.
次の例では,テロリズムに関連する言葉や経済に関連する言葉が,クリントン以降の大統領演説コーパスでどのように異なるかを見てみます.
recentCorpus <- corpus_subset(data_corpus_inaugural, Year > 1991)
テロリズムと経済という2つのリストからなる辞書を作成します.
myDict <- dictionary(list(terror = c("terrorism", "terrorists", "threat"),
economy = c("jobs", "business", "grow", "work")))
文書行列を作成するときに,この辞書をdfm()
のdictionary
に渡します.
byPresMat <- dfm(recentCorpus, dictionary = myDict)
byPresMat
## Document-feature matrix of: 7 documents, 2 features (14.3% sparse).
## 7 x 2 sparse Matrix of class "dfm"
## features
## docs terror economy
## 1993-Clinton 0 8
## 1997-Clinton 1 8
## 2001-Bush 0 4
## 2005-Bush 1 6
## 2009-Obama 1 10
## 2013-Obama 1 6
## 2017-Trump 1 5
dictionary()
は,LIWCやWordstatなどの一般的な辞書ファイルを読み込むことができますです.以下では,LIWCの辞書を大統領就任演説コーパスに適用しています.
liwcdict <- dictionary(file = "~/Dropbox/QUANTESS/dictionaries/LIWC/LIWC2001_English.dic",
format = "LIWC")
liwcdfm <- dfm(data_corpus_inaugural[52:58], dictionary = liwcdict)
liwcdfm[, 1:10]
presDfm <- dfm(corpus_subset(data_corpus_inaugural, Year > 1980),
remove = stopwords("english"), stem = TRUE, remove_punct = TRUE)
obamaSimil <- textstat_simil(presDfm, c("2009-Obama" , "2013-Obama"),
margin = "documents", method = "cosine")
obamaSimil
## 2009-Obama 2013-Obama
## 2009-Obama 1.0000000 0.6815711
## 2013-Obama 0.6815711 1.0000000
## 1981-Reagan 0.6229949 0.6376412
## 1985-Reagan 0.6434472 0.6629428
## 1989-Bush 0.6253944 0.5784290
## 1993-Clinton 0.6280946 0.6265428
## 1997-Clinton 0.6593018 0.6466030
## 2001-Bush 0.6018113 0.6193608
## 2005-Bush 0.5266249 0.5867178
## 2017-Trump 0.5192075 0.5160104
# dotchart(as.list(obamaSimil)$"2009-Obama", xlab = "Cosine similarity")
上記の文書間の類似性から樹形図を作成して,大統領を分類することができます.
data(data_corpus_SOTU, package = "quantedaData")
presDfm <- dfm(corpus_subset(data_corpus_SOTU, Date > as.Date("1980-01-01")),
stem = TRUE, remove_punct = TRUE,
remove = stopwords("english"))
presDfm <- dfm_trim(presDfm, min_count = 5, min_docfreq = 3)
# hierarchical clustering - get distances on normalized dfm
presDistMat <- textstat_dist(dfm_weight(presDfm, "relfreq"))
# hiarchical clustering the distance object
presCluster <- hclust(presDistMat)
# label with document names
presCluster$labels <- docnames(presDfm)
# plot as a dendrogram
plot(presCluster, xlab = "", sub = "", main = "Euclidean Distance on Normalized Token Frequency")
文書間と同様に用語間の類似性も測定できます.
sim <- textstat_simil(presDfm, c("fair", "health", "terror"), method = "cosine", margin = "features")
lapply(as.list(sim), head, 10)
## $fair
## economi begin jefferson author faith call struggl
## 0.9080252 0.9075951 0.8981462 0.8944272 0.8866586 0.8608285 0.8451543
## best creat courag
## 0.8366600 0.8347300 0.8326664
quantedaにはたくさんの計量テキスト分析のためのモデルが含まれていますが,ワードフィッシュ(textmodel_wordfish()
)による教師なしの文書のスケーリングを使ってみます.
# make prettier document names
ieDfm <- dfm(data_corpus_irishbudget2010)
textmodel_wordfish(ieDfm, dir = c(2, 1))
## Fitted wordfish model:
## Call:
## textmodel_wordfish.dfm(x = ieDfm, dir = c(2, 1))
##
## Estimated document positions:
##
## Documents theta SE lower
## 1 2010_BUDGET_01_Brian_Lenihan_FF 1.8209499 0.02032336 1.78111612
## 2 2010_BUDGET_02_Richard_Bruton_FG -0.5932807 0.02818846 -0.64853012
## 3 2010_BUDGET_03_Joan_Burton_LAB -1.1136790 0.01540265 -1.14386823
## 4 2010_BUDGET_04_Arthur_Morgan_SF -0.1219288 0.02846330 -0.17771690
## 5 2010_BUDGET_05_Brian_Cowen_FF 1.7724200 0.02364085 1.72608396
## 6 2010_BUDGET_06_Enda_Kenny_FG -0.7145798 0.02650268 -0.76652507
## 7 2010_BUDGET_07_Kieran_ODonnell_FG -0.4844834 0.04171492 -0.56624464
## 8 2010_BUDGET_08_Eamon_Gilmore_LAB -0.5616683 0.02967373 -0.61982877
## 9 2010_BUDGET_09_Michael_Higgins_LAB -0.9703126 0.03850567 -1.04578369
## 10 2010_BUDGET_10_Ruairi_Quinn_LAB -0.9589248 0.03892398 -1.03521585
## 11 2010_BUDGET_11_John_Gormley_Green 1.1807221 0.07221440 1.03918189
## 12 2010_BUDGET_12_Eamon_Ryan_Green 0.1866473 0.06294127 0.06328239
## 13 2010_BUDGET_13_Ciaran_Cuffe_Green 0.7421930 0.07245424 0.60018268
## 14 2010_BUDGET_14_Caoimhghin_OCaolain_SF -0.1840748 0.03666277 -0.25593382
## upper
## 1 1.86078371
## 2 -0.53803137
## 3 -1.08348984
## 4 -0.06614078
## 5 1.81875610
## 6 -0.66263457
## 7 -0.40272216
## 8 -0.50350776
## 9 -0.89484148
## 10 -0.88263383
## 11 1.32226236
## 12 0.31001215
## 13 0.88420329
## 14 -0.11221576
##
## Estimated feature scores: showing first 30 beta-hats for features
##
## when i presented the
## -0.09921416 0.38801227 0.39878221 0.25593308
## supplementary budget to this
## 1.11585345 0.09914364 0.37006803 0.30692462
## house last april ,
## 0.19905962 0.28970709 -0.09527213 0.34534182
## said we could work
## -0.71932033 0.47991222 -0.52977465 0.58226316
## our way through period
## 0.74372383 0.33610515 0.65981764 0.55620779
## of severe economic distress
## 0.33931211 1.27909804 0.47866025 1.84453970
## . today can report
## 0.27351503 0.17418685 0.36377507 0.69175117
## that notwithstanding
## 0.08832455 1.84453970
convert()
を用いると,dfmをtopicmodelsのLDA()
形式のデータに転換して,簡単にトピックモデルを適用できます.
quantdfm <- dfm(data_corpus_irishbudget2010,
remove_punct = TRUE, remove_numbers = TRUE, remove = stopwords("english"))
quantdfm <- dfm_trim(quantdfm, min_count = 4, max_docfreq = 10)
quantdfm
## Document-feature matrix of: 14 documents, 1,263 features (64.5% sparse).
if (require(topicmodels)) {
myLDAfit20 <- LDA(convert(quantdfm, to = "topicmodels"), k = 20)
get_terms(myLDAfit20, 5)
}
## Loading required package: topicmodels
## Topic 1 Topic 2 Topic 3 Topic 4 Topic 5
## [1,] "failed" "kind" "society" "welfare" "levy"
## [2,] "strategy" "imagination" "enterprising" "system" "million"
## [3,] "needed" "policies" "sense" "taxation" "carbon"
## [4,] "ministers" "wit" "equal" "fáil" "colleagues"
## [5,] "system" "face" "nation" "live" "placing"
## Topic 6 Topic 7 Topic 8 Topic 9 Topic 10
## [1,] "alternative" "fianna" "reduction" "fianna" "spending"
## [2,] "citizenship" "fáil" "million" "prsi" "measures"
## [3,] "wealth" "national" "investment" "earning" "pension"
## [4,] "adjustment" "irish" "rates" "taoiseach" "million"
## [5,] "breaks" "support" "scheme" "top" "review"
## Topic 11 Topic 12 Topic 13 Topic 14 Topic 15 Topic 16
## [1,] "welfare" "child" "measures" "taoiseach" "million" "taoiseach"
## [2,] "system" "benefit" "support" "fine" "welfare" "employees"
## [3,] "parties" "today" "investment" "gael" "support" "rate"
## [4,] "child" "welfare" "recovery" "may" "back" "referred"
## [5,] "sinn" "per" "action" "irish" "continue" "debate"
## Topic 17 Topic 18 Topic 19 Topic 20
## [1,] "sustained" "benefit" "care" "fianna"
## [2,] "person" "day" "welfare" "child"
## [3,] "believe" "fáil" "per" "taxes"
## [4,] "raising" "lenihan" "allowance" "earning"
## [5,] "continue" "bank" "hit" "better"