May 2018
Intermediate to advanced
228 pages
4h 11m
Japanese
Content preview from Rによるテキストマイニング ―tidytextを活用したデータ分析と可視化の基礎
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86
■
5
章 未整理形式へ（から）の変換
ylab("Contribution to sentiment") +
coord_flip()
図5-2 ポジティブ、ネガティブな感情を最も多く呼び起こしているAP記事の単語。AFINN
辞書のセンチメントスコアと出現頻度の積に基づいて比較している。
5.1.2
dfm
オブジェクトの整理
DTM
の別の実装を提供しているテキストマイニングパッケージもあります。たと
えば、
quanteda
パッケージ（
Benoit and Nulty 2016
）は
dfm
（
document-feature matrix
、
文書
-
特徴量行列）クラスを提供しています。
quanteda
パッケージには、大統領の就
任演説のコーパスが付属しており、適切な関数を使って
dfm
に変換できます。
library(methods)
data("data_corpus_inaugural", package = "quanteda")
inaug_dfm <- quanteda::dfm(data_corpus_inaugural, verbos
e = FALSE)
inaug_dfm
5.1
DTM
の整理
■
87
## Document-feature matrix of: 58 documents, ...
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