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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30
■
2
章 整理データを使ったセンチメント分析
図2-6 ジェーン・オースティンの小説で頻出するポジティブな単語とネガティブな単語
図2-6 の単語の文字サイズは、それぞれの感情の中での出現頻度に比例していま
す。この図は、最も重要なポジティブな単語、ネガティブな単語を知るためには役
立ちますが、単語の文字サイズで感情は比較できません。
2.6
単語を越えた単位
単語レベルのトークン化でも役に立つ仕事はたくさんできますが、別の単位を
使った方が役に立つ場合や別の単位がどうしても必要とされる場合もあります。た
とえば、センチメント分析アルゴリズムの中には、ユニグラム（
1
つの単語）を越え
て、文全体の感情を理解しようとするものがあります。この種のアルゴリズムは、
「
I am not having a good day
」は否定文なので、幸せな感情の文ではなく、悲し
い感情の文だと解釈しようとするわけです。
coreNLP
（
Arnold and Tilton 201
6
）、
cleanNLP
（
Arnold 2016
）、
sentimentr
（
Rinker 2017
）といった
R
パッケージは、その
2.6
単語を越えた単位
■
31
ようなセンチメント分析アルゴリズムの例です。これらを使うためには、テキスト
をセンテンス ...
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