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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118
■
6
章 トピックモデリング
mutate(term = reorder(term, beta)) %>%
ggplot(aes(term, beta, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip()
4
つのトピックは
4
冊の本に明確に対応しています。「
captain
」、「
nautilus
」、「
sea
」、
「
nemo
」のトピックが『海底二万里』であり、「
jane
」、「
darcy
」、「
elizabeth
」のトピッ
クが『高慢と偏見』であることは間違いありません。「
pip
」、「
joe
」が『大いなる遺産』、
「
martians
」、「
black
」、「
night
」が『宇宙戦争』だということもわかります。そして、
LDA
が「ファジーなクラスタリング」アルゴリズムで
あることを反映して、トピック
1
と
4
の「
miss
」、トピック
3
と
4
の「
time
」のように、複数のトピックに共通する頻出
語も含まれていることがわかります。
6.2.2
文書ごとの分類
この分析の個々の文書は、
1
つの章を表しています。そこで、個々の文書のトピッ
クが何かを知り、章を集めて正しい本を復元したいところです。これは、文書
-
ト
ピック確率のγ を調べればわかります。
chapters_gamma <- tidy(chapters_lda, matrix = "gamma") ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.