Scraping twitter data to visualize trending tweets in Kuala Lumpur
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(Disclaimer: I’ve no grudge against python programming language per se. I think its equally great. In the following post, I’m merely recounting my experience.)
It’s been quite a while since I last posted. The reasons are numerous, notable being, unable to decide which programming language to choose for web data scraping. The contenders were data analytic maestro, R
and data scraping guru, python
. So, I decided to give myself some time to figure out which language will be best for my use case. My use case was, Given some search keywords, scrape twitter for related posts and visualize the result. First, I needed the live data. Again, I was at the cross-roads, “R or Python”. Apparently python has some great packages for twitter data streaming like twython
,python-twitter
, tweepy
. Equivalent R libraries are twitteR
,rwteet
. I chose the rtweet
package for data collection over python for following reasons;
- I do not have to create a
credential file
(unlike in python) to log in to my twitter account. However, you do need to authenticate the twitter account when using thertweet
package. This authentication is done just once if using thertweet
package. Your twitter credentials will be stored locally. - Coding and code readability is far more easier as compared to python.
- The
rtweet
package allows for multiple hash tags to be searched for. - To localize the data, the package also allows for specifying geographic coordinates.
So, using the following code snippet, I was able to scrape data. The code has following parts;
-
A custom search for tweets function which will accept the search string. If search string is
NULL
, it will throw a message and stop, else it will search for hash tags specified in search string and return a data frame as output.#required libraries
> library(rtweet) > library(tidytext) > library(tidyverse) > library(stringr) > library(stopwords) > library(rtweet) # for search_tweets() # Create a function that will accept multiple hashtags and will search the twitter api for related tweets > search_tweets_queries <- function(x, n = 100, ...) { ## check inputs stopifnot(is.atomic(x), is.numeric(n)) if (length(x) == 0L) { stop("No query found", call. = FALSE) } ## search for each string in column of queries rt <- lapply(x, search_tweets, n = n, ...) ## add query variable to data frames rt <- Map(cbind, rt, query = x, stringsAsFactors = FALSE) ## merge users data into one data frame rt_users <- do.call("rbind", lapply(rt, users_data)) ## merge tweets data into one data frame rt <- do.call("rbind", rt) ## set users attribute attr(rt, "users") <- rt_users ## return tibble (validate = FALSE makes it a bit faster) tibble::as_tibble(rt, validate = FALSE) }
-
A data frame containing the search terms. Note, here my search hash-tags are
KTM
,MRT
andmonorail
.# create data frame with query column > df_query <- data.frame( query = c("KTM", "monorail","MRT"), n = rnorm(3), # change this number according to the number of searchwords in parameter query. As of now, the parameter got 3 keywords, therefore this nuber is set to 3. stringsAsFactors = FALSE )
-
Using the
search_tweets_queries
defined in step 1, to search for tweets. Note, the usage ofretryonratelimit=TRUE
indicates if search rate limit reached, then the crawler will sleep for a while and start again. Refer to thertweet
documentation for more information.# pass query column to the search_tweet_queries function defined above > df_collect_tweets <- search_tweets_queries(df_query$query, include_rts = FALSE, retryonratelimit = TRUE, #geocode for Kuala Lumpur geocode = "3.14032,101.69466,93.5mi" )
-
Once the data is collected, I’ll keep some selected columns only.
# Select and keep only relevant columns > df_select_tweets<- df_collect_tweets %>% select(c(user_id,created_at,screen_name, !is.na(hashtags),text, source,display_text_width>0,lang,!is.na(place_name), !is.na(place_full_name), !is.na(geo_coords), !is.na(country), !is.na(location), retweet_count,account_created_at, account_lang, query) )
-
Text mining: The collected data need to be cleaned. Therefore, I’ve used the basic
gsub()
function andstr_replace_all()
from thestringr
library.# Saving the selected columns data > df_select_tweets_1 = data.frame(lapply(df_select_tweets, as.character), stringsAsFactors=FALSE) ### Text preprocessing # 1. Remove URL from text # collapse to long format > clean_tweet<- df_select_tweets_1 #clean_tweet<- paste(df_select_tweets_1, collapse=" ") > clean_tweet$text = gsub("&", "", clean_tweet$text) > clean_tweet$text = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", clean_tweet$text) > clean_tweet$text = gsub("@\\w+", "", clean_tweet$text) > clean_tweet$text = gsub("[[:punct:]]", "", clean_tweet$text) > clean_tweet$text = gsub("[[:digit:]]", "", clean_tweet$text) > clean_tweet$text = gsub("http\\w+", "", clean_tweet$text) > clean_tweet$text = gsub("[ \t]{2,}", "", clean_tweet$text) > clean_tweet$text = gsub("^\\s+|\\s+$", "", clean_tweet$text) #get rid of unnecessary spaces > clean_tweet$text <- str_replace_all(clean_tweet$text," "," ") # Get rid of URLs > clean_tweet$text<- str_replace_all(clean_tweet$text, "https://t.co/[a-z,A-Z,0-9]*","") > clean_tweet$text<- str_replace_all(clean_tweet$text, "http://t.co/[a-z,A-Z,0-9]*","") # Take out retweet header, there is only one > clean_tweet$text <- str_replace(clean_tweet$text,"RT @[a-z,A-Z]*: ","") # Get rid of hashtags > clean_tweet$text <- str_replace_all(clean_tweet$text,"#[a-z,A-Z]*","") # Get rid of references to other screennames > clean_tweet$text <- str_replace_all(clean_tweet$text,"@[a-z,A-Z]*","")
a. Next, I’ll use the
tidytext
library fortoken
extraction# Unnest the tokens > df.clean<- clean_tweet %>% unnest_tokens(word, text) > clean_tweets<- tibble() > clean_tweets<- rbind(clean_tweets, df.clean) # Basic calculations # calculate word frequency > word_freq <- clean_tweets %>% count(word, sort=TRUE) > word_freq # A tibble: 5,291 x 2 wordn <chr> <int> 1 mrt 596 2 ktm 582 3 ke455 4 kl259 5 ni251 6 naik 221 7 the 214 8 at208 9 sentral 195 10 nak 193 # ... with 5,281 more rows
b. It should be noted, the national language of Malaysia is
Bahasa Melayu (BM)
. To remove the stop words in BM, I’ve used thestopwords
library. lots of stop words like the, and, to, a etc. Let’s remove the stop words. We can remove the stop words from our tibble with anti_join and the built-in stop_words data set provided by tidytext.> clean_tweets %>% # remove the stopwords in Bahasa Melayu (BM). Use `ms` for BM. See this reference for other language codes: https://en.wikipedia.org/wiki/ISO_639-1 anti_join(get_stopwords(language="ms", source="stopwords-iso")) %>% # remove the stopwords in english anti_join(get_stopwords(language="en", source="stopwords-iso")) %>% count(word, sort=TRUE) %>% top_n(10) %>% ggplot(aes(word,n, fill=word))+ geom_bar(stat = "identity")+ xlab(NULL)+ ylab(paste('Word count'))+ ggtitle(paste('Most common words in tweets')) + theme(legend.position="none") + theme_minimal()+ coord_flip()
-
Finally, I present a basic bar plot to show the trending words.
Barplot: Trending twitter words in kuala lumpur, malaysia
Area’s of further improvement
- How to extract tweets within a given time range?
See the code on my Github account
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