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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106
■
6
章 トピックモデリング
整理された
モデル
コーパス
オブジェクト
コーパス
オブジェクト
テキスト
集計データ
テキスト
データ
可視化
整理テキスト
センチメント
辞書
DTM
コーパス
オブジェクト
モデル
unnest_tokens
tidytext
count
dplyr
ggplot2
inner_join
dplyr
tidy
tidytext
LDA
topicmodels
tm
quanteda
tidy
tidytext
tm,
quanteda
cast_dtm
cast_dfm
tidytext
tidy
tidytext
dplyr, tidyr
tm, quanteda
dplyr, tidyr
ggplot2
group_by
summarize
dplyr
dplyr, tidyr
図6-1 トピックモデリングを組み込んだテキスト分析のフローチャート。topicmodelsパッ
ケージは、DTMを入力とし、tidytextで整理できるモデルを出力する。整理されたモ
デルは、dplyrや ggplot2で操作、可視化することができる。
6.1
LDA
LDA
は、トピックモデリングで最もよく使われているアルゴリズムの
1
つです。
モデルの背後の数学に首を突っ込まなくても、
LDA
が次の
2
つの原則に導かれてい
ることは理解できます。
すべての文書はトピック（テーマ）の組み合わせ
個々の文書は、特定の割合で複数のトピックに関連する単語が含まれたも
のだと考えることができます。たとえば、
2
トピックモデルでは、「文書
1
はトピック
A
が
90
％、
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.