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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8.4
トピックモデリング
■
167
ることができます。このトピックモデリングでは、今までの章と同様に、
LDA
（潜
在的ディリクレ配分法、
https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
）
を使うこととします。トピックモデリングには、これ以外のアプローチもあります。
8.4.1
DTM
へのキャスト
これから説明する方法でトピックモデリングを行うためには、
tm
パッケージで
定義されている
DocumentTermMatrix
という特別な行列が必要です（もちろん、これ
は
DTM
（文書
-
単語行列）という一般的な概念の実装の
1
つにすぎません）。行は文
書（ここでは説明フィールド）、列は単語に対応しています。
DTM
は疎行列であり、
値は語数です。
まず、ストップワードを使って、
HTML
その他の文字エンコーディングによって
残された無意味な「単語」を取り除き、
テキストを少しクリーンアップしましょう。
bind_rows()
を使えば、
tidytext
パッケージのデフォルトストップワードリストにカ
スタムストップワードを追加できます。そのあとで
anti_join()
を使えば、データ
フレームから両方のストップワードをまとめて取り除くことができます。
my_stop_words <- bind_rows(stop_words,
data_frame(word = c("nbsp", "amp", "gt", "lt",
"timesnewromanpsmt", ...
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