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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9.3
センチメント分析
■
197
## 6 soc.religion.christian faith 257 1 0.004040817
## 7 talk.religion.misc jesus 256 1 0.004025094
## 8 talk.politics.mideast killed 202 -3 -0.009528152
## 9 talk.politics.mideast war 187 -2 -0.005880411
## 10 soc.religion.christian true 179 2 0.005628842
## # ... with 13,053 more rows
この結果からは、
misc.forsale
ニュースグループについてのわれわれの仮説が正し
かったことが確かめる
ことができます。ポジティブな感情の大半は、「
excellent
」、
「
perfect
」といった形容詞によるものです。それに対し、
atheism
（無神論）ニュース
グループは、ネガティブなコンテキストで「
god
」を詳細に論じているはずであり、
単語「
god
」がこのニュースグループを実際よりもポジティブに見せていることがわ
かります。同様に、
talk.politics.guns
グループの単語「
gun
」（銃）は、メンバーが銃
を肯定する立場で議論していても、ネガティブなセンチメントスコアの上昇に寄与
しています。
センチメント分析はトピック次第で混乱することがあるということを改めて思い
知らされたわ
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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.