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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9.2
ニュースグループに含まれる単語
■
189
図9-3 頻出語の相関に基づいて描いたUsenetグループのネットワーク。相関が 0.4よりも
大きいものだけを描いている。
ニュースグループには、コンピュータ
/
電子工学、政治
/
宗教、自動車
/
バイク、
スポーツの
4
つの大きなクラスタがあるようです。これらのニュースグループに期
待した共通の単語と話題を示したことを考えれば、納得できる結果です。
9.2.2
トピックモデリング
第
6
章では、
LDA
（潜在的ディリクレ配分法）を使って、一連の章を分類してもと
もとどの本に含まれていたのかを推測しました。
LDA
は、さまざまなニュースグ
ループに含まれている
Usenet
メッセージも同じように分類できるでしょうか。
4
つの科学関連ニュースグループのメッセージを分類してみましょう。まず、
cast_dtm()
でメッセージを
DTM
にまとめてから（「5.2 整理データの行列へのキャ
190
■
9
章 ケーススタディ：
Usenet
テキストの分析
スト」参照）、
topicmodels
パッケージの
LDA()
関数でモデルを作り、単語を分類して
みましょう。
#
出現頻度が
50
回以上の単語のみを抽出
word_sci_newsgroups <- usenet_words %>%
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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.