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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50
■
3
章 単語の出現頻度と特定の文書での出現頻度の分析：
tf-idf
ggplot(plot_physics, aes(word, tf_idf, fill = author)) +
geom_col(show.legend = FALSE) +
labs(x = NULL, y = "tf-idf") +
facet_wrap(~author, ncol = 2, scales = "free") +
coord_flip()
図3-6 物理学の古典に含まれるtf-idfの高い単語
図3-6 からわかるように、今日の物理学においては、城壁（
ramparts
）という言葉
や「物質はエーテルである」という発言は聞くことはないでしょう。
3.5
まとめ
tf-idf
（単語出現頻度と逆文書頻度）を使うと、小説でも物理学の文書でも
Web
ペー
ジでも、文書のコレクションの中にある
1
つの文書で特徴的な単語を探し出すこと
ができます。単語出現頻度だけを掘り下げていっても、自然言語のコレクションの
中で言語がどのように使われているかについての知見が得られます。そして、
dplyr
3.5
まとめ
■
51
の
count()
、
rank()
などを使えば、単語出現頻度についてより深く知ることができ
ます。
tidytext ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.