Follow-Up: Making a Word Cloud for a Search Result from GScholar_Scraper_3.1

August 30, 2012
By

[This article was first published on theBioBucket*, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Here’s a short follow-up on how to produce a word cloud for a search result from GScholarScraper_3.1:

# File-Name: GScholarScraper_3.1.R
# Date: 2012-08-22
# Author: Kay Cichini
# Email: [email protected]
# Purpose: Scrape Google Scholar search result
# Packages used: XML
# Licence: CC BY-SA-NC
#
# Arguments:
# (1) input:
# A search string as used in Google Scholar search dialog
#
# (2) write:
# Logical, should a table be writen to user default directory?
# if TRUE ("T") a CSV-file with hyperlinks to the publications will be created.
#
# Difference to version 3:
# (3) added "since" argument - define year since when publications should be returned..
# defaults to 1900..
#
# (4) added "citation" argument - logical, if "0" citations are included
# defaults to "1" and no citations will be included..
# added field "YEAR" to output
#
# Caveat: if a submitted search string gives more than 1000 hits there seem
# to be some problems (I guess I'm being stopped by Google for roboting the site..)
#
# And, there is an issue with this error message:
# > Error in htmlParse(URL):
# > error in creating parser for http://scholar.google.com/scholar?q
# I haven't figured out his one yet.. most likely also a Google blocking mechanism..
# Reconnecting / new IP-address helps..


GScholar_Scraper <- function(input, since = 1900, write = F, citation = 1) {

require(XML)

# putting together the search-URL:
URL <- paste("http://scholar.google.com/scholar?q=", input, "&as_sdt=1,5&as_vis=",
citation, "&as_ylo=", since, sep = "")
cat("\nThe URL used is: ", "\n----\n", paste("* ", "http://scholar.google.com/scholar?q=", input, "&as_sdt=1,5&as_vis=",
citation, "&as_ylo=", since, " *", sep = ""))

# get content and parse it:
doc <- htmlParse(URL)

# number of hits:
h1 <- xpathSApply(doc, "//div[@id='gs_ab_md']", xmlValue)
h2 <- strsplit(h1, " ")[[1]][2]
num <- as.integer(sub("[[:punct:]]", "", h2))
cat("\n\nNumber of hits: ", num, "\n----\n", "If this number is far from the returned results\nsomething might have gone wrong..\n\n", sep = "")

# If there are no results, stop and throw an error message:
if (num == 0 | is.na(num)) {
stop("\n\n...There is no result for the submitted search string!")
}

pages.max <- ceiling(num/100)

# 'start' as used in URL:
start <- 100 * 1:pages.max - 100

# Collect URLs as list:
URLs <- paste("http://scholar.google.com/scholar?start=", start, "&q=", input,
"&num=100&as_sdt=1,5&as_vis=", citation, "&as_ylo=", since, sep = "")

scraper_internal <- function(x) {

doc <- htmlParse(x, encoding="UTF-8")

# titles:
tit <- xpathSApply(doc, "//h3[@class='gs_rt']", xmlValue)

# publication:
pub <- xpathSApply(doc, "//div[@class='gs_a']", xmlValue)

# links:
lin <- xpathSApply(doc, "//h3[@class='gs_rt']/a", xmlAttrs)

# summaries are truncated, and thus wont be used..
# abst <- xpathSApply(doc, '//div[@class='gs_rs']', xmlValue)
# ..to be extended for individual needs
options(warn=(-1))
dat <- data.frame(TITLES = tit, PUBLICATION = pub,
YEAR = as.integer(gsub(".*\\s(\\d{4})\\s.*", "\\1", pub)),
LINKS = lin)
options(warn=0)
return(dat)
}

result <- do.call("rbind", lapply(URLs, scraper_internal))
if (write == T) {
result$LINKS <- paste("=Hyperlink(","\"", result$LINKS, "\"", ")", sep = "")
write.table(result, "GScholar_Output.CSV", sep = ";",
row.names = F, quote = F)
shell.exec("GScholar_Output.CSV")
} else {
return(result)
}
}

# EXAMPLE:

input <- "allintitle:amphibian+diversity"
df <- GScholar_Scraper(input, since = 1980, citation = 1)

#install.packages("tm")
library(tm)

#install.packages("wordcloud")
library(wordcloud)

corpus <- Corpus(VectorSource(df$TITLES))
corpus <- tm_map(corpus, function(x)removeWords(x, c(stopwords(), "PDF", "B", "DOC", "HTML", "BOOK", "CITATION")))
corpus <- tm_map(corpus, removePunctuation)
tdm <- TermDocumentMatrix(corpus)
m <- as.matrix(tdm)
v <- sort(rowSums(m), decreasing = TRUE)
d <- data.frame(word = names(v), freq = v)

# remove numbers from strings:
d <- d[-grep("[0-9]", d$word), ]

# print wordcloud:
wordcloud(d$word, d$freq)

To leave a comment for the author, please follow the link and comment on their blog: theBioBucket*.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)