[英]How do I clean twitter data in R?
I extracted tweets from twitter using the twitteR package and saved them into a text file.我使用 twitteR 包从 twitter 中提取推文并将它们保存到文本文件中。
I have carried out the following on the corpus我已经对语料库进行了以下操作
xx<-tm_map(xx,removeNumbers, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,stripWhitespace, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,removePunctuation, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,strip_retweets, lazy=TRUE, 'mc.cores=1')
xx<-tm_map(xx,removeWords,stopwords(english), lazy=TRUE, 'mc.cores=1')
(using mc.cores=1 and lazy=True as otherwise R on mac is running into errors) (使用 mc.cores=1 和 lazy=True 否则 mac 上的 R 会遇到错误)
tdm<-TermDocumentMatrix(xx)
But this term document matrix has a lot of strange symbols, meaningless words and the like.但是这个词条文档矩阵有很多奇怪的符号,无意义的词等等。 If a tweet is如果一条推文是
RT @Foxtel: One man stands between us and annihilation: @IanZiering.
Sharknado‚Äã 3: OH HELL NO! - July 23 on Foxtel @SyfyAU
After cleaning the tweet I want only proper complete english words to be left , ie a sentence/phrase void of everything else (user names, shortened words, urls)清理推文后,我只想留下正确的完整英语单词,即一个没有其他所有内容的句子/短语(用户名、缩写词、网址)
example:例子:
One man stands between us and annihilation oh hell no on
(Note: The transformation commands in the tm package are only able to remove stop words, punctuation whitespaces and also conversion to lowercase) (注意:tm 包中的转换命令只能去除停用词、标点空格和小写转换)
Using gsub and使用 gsub 和
stringr package字符串包
I have figured out part of the solution for removing retweets, references to screen names, hashtags, spaces, numbers, punctuations, urls .我已经找到了删除转发、对屏幕名称、主题标签、空格、数字、标点符号、网址的引用的部分解决方案。
clean_tweet = gsub("&", "", unclean_tweet)
clean_tweet = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", clean_tweet)
clean_tweet = gsub("@\\w+", "", clean_tweet)
clean_tweet = gsub("[[:punct:]]", "", clean_tweet)
clean_tweet = gsub("[[:digit:]]", "", clean_tweet)
clean_tweet = gsub("http\\w+", "", clean_tweet)
clean_tweet = gsub("[ \t]{2,}", "", clean_tweet)
clean_tweet = gsub("^\\s+|\\s+$", "", clean_tweet)
ref: ( Hicks , 2014) After the above I did the below.参考:(希克斯,2014 年)在上述之后我做了以下。
#get rid of unnecessary spaces
clean_tweet <- str_replace_all(clean_tweet," "," ")
# Get rid of URLs
clean_tweet <- str_replace_all(clean_tweet, "http://t.co/[a-z,A-Z,0-9]*{8}","")
# Take out retweet header, there is only one
clean_tweet <- str_replace(clean_tweet,"RT @[a-z,A-Z]*: ","")
# Get rid of hashtags
clean_tweet <- str_replace_all(clean_tweet,"#[a-z,A-Z]*","")
# Get rid of references to other screennames
clean_tweet <- str_replace_all(clean_tweet,"@[a-z,A-Z]*","")
ref: (Stanton 2013)参考:(斯坦顿 2013)
Before doing any of the above I collapsed the whole string into a single long character using the below.在执行上述任何操作之前,我使用以下命令将整个字符串折叠成一个长字符。
paste(mytweets, collapse=" ")
This cleaning process has worked for me quite well as opposed to the tm_map transforms.与 tm_map 转换相比,这个清理过程对我来说非常有效。
All that I am left with now is a set of proper words and a very few improper words.我现在只剩下一套合适的词和一些不合适的词。 Now, I only have to figure out how to remove the non proper english words.现在,我只需要弄清楚如何删除不正确的英语单词。 Probably i will have to subtract my set of words from a dictionary of words.可能我将不得不从单词词典中减去我的一组单词。
library(tidyverse)
clean_tweets <- function(x) {
x %>%
# Remove URLs
str_remove_all(" ?(f|ht)(tp)(s?)(://)(.*)[.|/](.*)") %>%
# Remove mentions e.g. "@my_account"
str_remove_all("@[[:alnum:]_]{4,}") %>%
# Remove hashtags
str_remove_all("#[[:alnum:]_]+") %>%
# Replace "&" character reference with "and"
str_replace_all("&", "and") %>%
# Remove puntucation, using a standard character class
str_remove_all("[[:punct:]]") %>%
# Remove "RT: " from beginning of retweets
str_remove_all("^RT:? ") %>%
# Replace any newline characters with a space
str_replace_all("\\\n", " ") %>%
# Make everything lowercase
str_to_lower() %>%
# Remove any trailing whitespace around the text
str_trim("both")
}
tweets %>% clean_tweets
To remove the URLs you could try the following:要删除 URL,您可以尝试以下操作:
removeURL <- function(x) gsub("http[[:alnum:]]*", "", x)
xx <- tm_map(xx, removeURL)
Possibly you could define similar functions to further transform the text.也许您可以定义类似的函数来进一步转换文本。
For me, this code did not work, for some reason-对我来说,由于某种原因,这段代码不起作用-
# Get rid of URLs
clean_tweet <- str_replace_all(clean_tweet, "http://t.co/[a-z,A-Z,0-9]*{8}","")
Error was-错误是——
Error in stri_replace_all_regex(string, pattern, fix_replacement(replacement), :
Syntax error in regexp pattern. (U_REGEX_RULE_SYNTAX)
So, instead, I used所以,相反,我用
clean_tweet4 <- str_replace_all(clean_tweet3, "https://t.co/[a-z,A-Z,0-9]*","")
clean_tweet5 <- str_replace_all(clean_tweet4, "http://t.co/[a-z,A-Z,0-9]*","")
to get rid of URLs摆脱网址
The code do some basic cleaning代码做一些基本的清理
df <- tm_map(df, tolower)
df <- tm_map(df, removePunctuation)
df <- tm_map(df, removeNumbers)
df <- tm_map(df, removeWords, stopwords('english'))
removeURL <- function(x) gsub('http[[:alnum;]]*', '', x)
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