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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7.2
単語の出現頻度
■
131
7.2
単語の出現頻度
次に、
unnest_tokens()
を使ってツイートに含まれるすべての単語を整理データ
フレームに変換し、英語のストップワードを取り除きましょう。
Twitter
のテキスト
には、特別な慣習があるので、たとえば
Project Gutenberg
の小説を処理したときよ
りも少し余分に操作を加えます。
まず、データセットからリツイートでしかないツイートを取り除き、自分で書い
たツイートだけが残るようにします。次に、
mutate()
でリンクを取り除き、
&
など
の文字を取り除きます。
unnest_tokens()
関数では、ユニグラム（単語）を探すだけではなく、正
規表現によるトークン化を行っています。この正規表現パターンは、
Twitter
テキストを操作するときにはとても便利で、ハッシュタグや
@
記
号付きのユーザ名がそのまま残ります。
この種の記号をテキストに残してあるので、単純な
anti_join()
でストップワー
ドを取り除くわけにはいきません。代わりに、
stringr
パッケージの
str_detect()
を
使った
filter()
行のようなことを行っています。
library(tidytext)
library(stringr)
replace_reg1 <- "https://t.co/[A-Za-z\\d]+|"
replace_reg2 <- "http://[A-Za-z\\d]+|&|<|>|RT|https"
replace_reg ...
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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.