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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200
■
9
章 ケーススタディ：
Usenet
テキストの分析
## Andrew--
ここではセンチメント分析が見事に成功していると言えます。
9.3.3
n
グラム解析
第
4
章では、「
don
’
t like
」のようなフレーズのためにその部分が誤ってポジティブ
に分類されるようなことを考慮して、「
not
」や「
no
」などの単語が与える影響を計算
しながらジェーン・オースティンの小説のセンチメント分析を行いました。
Usenet
データセットは、ジェーン・オースティンの小説よりも新しいテキストをはるかに
大規模に集めたコーパスなので、センチメント分析がどのような形で逆になるかに
も興味がわきます。
まず、
Usenet
ポストに含まれるすべてのバイグラムを探して数えるところから始
めます。
usenet_bigrams <- cleaned_text %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2)
usenet_bigr
am_counts <- usenet_bigrams %>%
count(newsgroup, bigram, sort = TRUE) %>%
ungroup() %>%
separate(bigram, c("word1", "word2"), sep = " ")
次に、「
no
」、「
not
」、「
without
」など、否定のために使われる
6
つの単語のリスト
を定義し、これらの後ろに続くことが多い感情を表す単語を可視化します（図
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