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Authors:Julia Silge, David Robinson
License:MIT
Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like dplyr, broom, tidyr, and ggplot2. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages. Check out our book to learn more about text mining using tidy data principles.
Installation
You can install this package from CRAN:
Or you can install the development version from GitHub with remotes:
Tidy text mining example: the unnest_tokens function
The novels of Jane Austen can be so tidy! Let's use the text of Jane Austen's 6 completed, published novels from the janeaustenr package, and transform them to a tidy format. janeaustenr provides them as a one-row-per-line format:
To work with this as a tidy dataset, we need to restructure it as one-token-per-row format. The unnest_tokens() function is a way to convert a dataframe with a text column to be one-token-per-row:
This function uses the tokenizers package to separate each line into words. The default tokenizing is for words, but other options include characters, n-grams, sentences, lines, paragraphs, or separation around a regex pattern.
Now that the data is in a one-word-per-row format, we can manipulate it with tidy tools like dplyr. We can remove stop words (available via the function get_stopwords()) with an anti_join().
We can also use count() to find the most common words in all the books as a whole.
Sentiment analysis can be implemented as an inner join. Three sentiment lexicons are available via the get_sentiments() function. Let's examine how sentiment changes across each novel. Let's find a sentiment score for each word using the Bing lexicon, then count the number of positive and negative words in defined sections of each novel.
https://izenintu1973.mystrikingly.com/blog/best-steam-games-for-macbook-pro. Now we can plot these sentiment scores across the plot trajectory of each novel.
For more examples of text mining using tidy data frames, see the tidytext vignette.
Tidying document term matrices
Some existing text mining datasets are in the form of a DocumentTermMatrix class (from the tm package). For example, consider the corpus of 2246 Associated Press articles from the topicmodels dataset.
If we want to analyze this with tidy tools, we need to transform it into a one-row-per-term data frame first with a tidy() function. (For more on the tidy verb, see the broom package).
We could find the most negative documents:
Or we can join the Austen and AP datasets and compare the frequencies of each word:
For more examples of working with objects from other text mining packages using tidy data principles, see the vignette on converting to and from document term matrices.
Community Guidelines
This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. Feedback, bug reports (and fixes!), and feature requests are welcome; file issues or seek support here.
- 카테고리:Utilities
- 최근 업데이트: 2019-08-08
- 현재 버전: 2.7.4
- 파일 크기: 5.10 MB
- 개발자:Tunabelly Software Inc.
- 호환성: 필요 Windows XP, Windows Vista, Windows 7, Windows 8, Windows 8.1 and Windows 10
다운로드 링크
앱 다운로드 ⇩사용 Folder Tidy 귀하의 Windows 컴퓨터에서 실제로 매우 쉽습니다하지만 당신 이이 과정을 처음 접한다면, 당신은 분명히 아래 나열된 단계에주의를 기울일 필요가있을 것입니다. 컴퓨터 용 데스크톱 응용 프로그램 에뮬레이터를 다운로드하여 설치해야하기 때문입니다. 다운로드 및 설치를 도와 드리겠습니다 Folder Tidy 아래의 간단한 4 단계로 컴퓨터에서:Folder Tidy 2 7 5 X 8 Bathroom Layouts
1 : Windows 용 Android 소프트웨어 에뮬레이터 다운로드
에뮬레이터의 중요성은 컴퓨터에서 안드로이드 환경을 흉내 내고 안드로이드 폰을 구입하지 않고도 안드로이드 앱을 설치하고 실행하는 것을 매우 쉽게 만들어주는 것입니다. 누가 당신이 두 세계를 즐길 수 없다고 말합니까? 우선 아래에있는 에뮬레이터 앱을 다운로드 할 수 있습니다.
A. Nox App . OR
B. Bluestacks App.
Bluestacks는 매우 대중적이므로 개인적으로 'B'옵션을 사용하는 것이 좋습니다. 문제가 발생하면 Google 또는 Naver.com에서 좋은 해결책을 찾을 수 있습니다.
2 : 이제 Windows PC에 소프트웨어 에뮬레이터 설치
Bluestacks.exe 또는 Nox.exe를 성공적으로 다운로드 한 경우 컴퓨터의 다운로드 폴더 또는 다운로드 한 파일을 일반적으로 저장하는 곳으로 이동하십시오.
찾으면 클릭하여 응용 프로그램을 설치하십시오. PC에서 설치 프로세스를 시작해야합니다.
다음을 클릭하여 EULA 라이센스 계약에 동의하십시오.
응용 프로그램을 설치하려면 화면 지시문을 따르십시오.
위 사항을 올바르게 수행하면 소프트웨어가 성공적으로 설치됩니다.
3 : Windows PC 용 Folder Tidy 사용 방법 - Windows 7/8 / 8.1 / 10
이제 설치 한 에뮬레이터 애플리케이션을 열고 검색 창을 찾으십시오. 지금 입력하십시오. - Folder Tidy 앱을 쉽게 볼 수 있습니다. 그것을 클릭하십시오. 응용 프로그램 창이 열리고 에뮬레이터 소프트웨어에 응용 프로그램이 표시됩니다. 설치 버튼을 누르면 응용 프로그램이 다운로드되기 시작합니다. 이제 우리는 모두 끝났습니다.
그런 다음 '모든 앱'아이콘이 표시됩니다.
클릭하면 설치된 모든 응용 프로그램이 포함 된 페이지로 이동합니다.
당신은 Folder Tidy 상. 그것을 클릭하고 응용 프로그램 사용을 시작하십시오.
Folder Tidy 2 7 5 X 8 Outdoor Rug
보너스 : Android 및 iOS 용 다운로드
Android에서 다운로드
개발자 설명
(2019) APP STORE FEATURED STORY & (2014) MACWORLD GEMTake any disorganized folder and sort it all into tidy subfolders. Use the 15+ built-in rules, or create powerful custom ones to organize your files just the way you want. Folder Tidy uses all available CPU cores for the fastest possible tidying, and can handle TBs of files.With one click Folder Tidy will sort the files from the messy folder. Imagine having all the chaos sorted into tidy subfolders, such as Pictures, Music, Spreadsheets, Source Code, etc. Or with the custom rules, do things like move all pictures older than 2015 into 'Old Pictures', or move all Word documents that have the Finder tag of 'Work' into 'My Office Work Files'.If it turns out the tidy wasn't exactly the way you wanted it, no problem, just hit the Undo button and everything will be moved back to their original locations.Check out some of the awesome features of Folder Tidy:• Organize your files into subfolders based on the file type and/or rules.• Use one of the many built-in rules (18 and counting), or create powerful ones based on predicates.• Choose what types of files to clean up.• Choose files and folders to ignore during a clean up.• Choose where to put the cleaned up files and folders. • Options to ignore aliases, folders and anything with a label.• Ability to undo the clean up.• Hundreds of five star ratings internationally.Featured on the Mac App Store, Lifehacker, Addictive Tips, Softpedia, Mac360, MakeUseOf.com, Macworld and 9/10 on Mac.AppStorm.
