How do I clean twitter data in R? - r

Question

I extracted tweets from twitter using the twitteR package and saved them into a text file.
I have carried out the following on the corpus
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)
tdm<-TermDocumentMatrix(xx)
But this term document matrix has a lot of strange symbols, meaningless words and the like.
If a tweet is
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 , i.e a sentence/phrase void of everything else (user names, shortened words, urls)
example:
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)

Answer 1

Using gsub and
stringr package
I have figured out part of the solution for removing retweets, references to screen names, hashtags, spaces, numbers, punctuations, urls .
clean_tweet = gsub("&amp", "", 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.
#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)
Before doing any of the above I collapsed the whole string into a single long character using the below.
paste(mytweets, collapse=" ")
This cleaning process has worked for me quite well as opposed to the tm_map transforms.
All that I am left with now is a set of proper words and a very few improper words.
Now, I only have to figure out how to remove the non proper english words.
Probably i will have to subtract my set of words from a dictionary of words.

Answer 2

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

Answer 3

To remove the URLs you could try the following:
removeURL <- function(x) gsub("http[[:alnum:]]*", "", x)
xx <- tm_map(xx, removeURL)
Possibly you could define similar functions to further transform the text.

Answer 4

For me, this code did not work, for some reason-
# Get rid of URLs
clean_tweet <- str_replace_all(clean_tweet, "http://t.co/[a-z,A-Z,0-9]*{8}","")
Error was-
Error in stri_replace_all_regex(string, pattern, fix_replacement(replacement), :
Syntax error in regexp pattern. (U_REGEX_RULE_SYNTAX)
So, instead, I used
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

Answer 5

The code do some basic cleaning
Converts into lowercase
df <- tm_map(df, tolower)
Removing Special characters
df <- tm_map(df, removePunctuation)
Removing Special characters
df <- tm_map(df, removeNumbers)
Removing common words
df <- tm_map(df, removeWords, stopwords('english'))
Removing URL
removeURL <- function(x) gsub('http[[:alnum;]]*', '', x)