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4Chan-Web-Scraper-v2/scripts/4ChanScraperv2.R

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##### Code Below #####
#Load Libraries
library("rvest")
library("tidyverse")
library("ggplot2")
library("wordcloud")
library("tidytext")
library("tinytex")
library("syuzhet")
library("lubridate")
library("scales")
library("reshape2")
library("dplyr")
library("qdapDictionaries")
# library("tm") Dont need
# library("qdap") Dont need
# This scraping is getting all of the internal links
#Page 1 Scrape
pol_threads1 <- read_html("https://boards.4channel.org/pol/") %>%
html_elements("a") %>%
html_attr('href')
#Page 2 Scrape
pol_threads2 <- read_html("https://boards.4channel.org/pol/2") %>%
html_elements("a") %>%
html_attr('href')
#Page 3 Scrape
pol_threads3 <- read_html("https://boards.4channel.org/pol/3") %>%
html_elements("a") %>%
html_attr('href')
#Page 4 Scrape
pol_threads4 <- read_html("https://boards.4channel.org/pol/4") %>%
html_elements("a") %>%
html_attr('href')
#Page 5 Scrape
pol_threads5 <- read_html("https://boards.4channel.org/pol/5") %>%
html_elements("a") %>%
html_attr('href')
#Page 6 Scrape
pol_threads6 <- read_html("https://boards.4channel.org/pol/6") %>%
html_elements("a") %>%
html_attr('href')
#Page 7 Scrape
pol_threads7 <- read_html("https://boards.4channel.org/pol/7") %>%
html_elements("a") %>%
html_attr('href')
#Page 8 Scrape
pol_threads8 <- read_html("https://boards.4channel.org/pol/8") %>%
html_elements("a") %>%
html_attr('href')
#Page 9 Scrape
pol_threads9 <- read_html("https://boards.4channel.org/pol/9") %>%
html_elements("a") %>%
html_attr('href')
#Page 10 Scrape
pol_threads10 <- read_html("https://boards.4channel.org/pol/10") %>%
html_elements("a") %>%
html_attr('href')
# get_hrefs <- . %>% read_html %>% html_elements("a") %>%
# html_attr('href')
# paste("https://boards.4channel.org/pol/", 1:9) %>% map_chr(get_hrefs)
# Combining all of the threads into 1 data frame
df_pol <- c(pol_threads1,
pol_threads2,
pol_threads3,
pol_threads4,
pol_threads5,
pol_threads6,
pol_threads7,
pol_threads8,
pol_threads9,
pol_threads10)
#tibble makes a table out of data from the scraped links
pol_table <- tibble(txt = df_pol)
# Choosing all of the links that look like: "thread/this-is-a-thread"
df_links <- pol_table %>%
filter(str_detect(txt, "(thread/[0-9]{6,}[/][a-z]{1,})"))
# Next step is appending on "https://boards.4chan.org/pol/" before the "thread/this-is-a-thread".
df_links$txt <- paste("https://boards.4chan.org/pol/", df_links$txt, sep = "")
# This code will "apply" the
# "read_html"
# "html_elements" and
# "html_text"
# to each row in the data frame
threads <- lapply(df_links$txt, function(x) {
read_html(x) %>%
html_text2()})
# Turn "threads" into a tibble so tidytex can manipulate it
threads_tibble <- tibble(txt = threads)
# Break up all of the sentences into single words
tidy_pol <- threads_tibble %>%
unnest_tokens(word, txt, format = "text")
# Doesn't perform well
# https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/R_text_3_quanteda.md
# tidy_pol <- removeNumbers(tidy_pol$word) %>%
# removePunctuation() %>%
# stripWhitespace()
# threads_tibble <- tibble(txt = tidy_pol)
# tidy_pol <- threads_tibble %>%
# unnest_tokens(word, txt, format = "text")
# mutate(token_stem = wordStem(word))
# tidy_pol_test <- as.list(tidy_pol)
# tidy_pol_test2 <- tokens(tidy_pol_test)
# tidy_pol_test3 <- tokens_wordstem(tidy_pol_test2)
# tokens_rbind <- do.call(rbind, tidy_pol_test3)
# tokens_col <- tibble(txt = tokens_rbind)
# tokens_col_all <-data.frame(txt = c(t(tokens_col)), stringsAsFactors=FALSE)
# Put this in the ngram script
#filter(!grepl('[0-9]', word))
tidy_pol_fixed <- tidy_pol %>%
filter(!word %in% stop_words$word
& !word == "fucking"
& !word == "https"
& !word == "shit"
& !word == "id"
& !word == "anonymous"
& !word == "wed"
& !word == "kb"
& !word == "var"
& !word == "png"
& !word == "mobile"
& !word == "mb"
& !word == "catalog"
& !word == "settings"
& !word == "display"
& !word == "advertise"
& !word == "pass"
& !word == "bottom"
& !word == "pol"
& !word == "shit"
& !word == "jpg"
& !word == "view"
& !word == "vp"
& !word == "ad"
& !word == "tv"
& !word == "fit"
& !word == "post"
& !word == "thread"
& !word == "hr"
& !word == "gif"
& !word == "webm"
& !word == "incorrect"
& !word == "tg"
& !word == "comments"
& !word == "search"
& !word == "top"
& !word == "site"
& !word == "home"
& !word == "reply"
& !word == "board"
& !word == "politically"
& !word == "return"
& !word == "time"
& !word == "owned"
& !word == "added"
& !word == "vip"
& !word == "users"
& !word == "rules"
& !word == "legal"
& !word == "lgbt"
& !word == "lit"
& !word == "file"
& !word == "mu"
& !word == "hide"
& !word == "fa"
& !word == "responsibility"
& !word == "style"
& !word == "options"
& !word == "table"
& !word == "page"
& !word == "serve"
& !word == "contact"
& !word == "images"
& !word == "international"
& !word == "poster"
& !word == "people"
& !word == "true"
& !word == "bant"
& !word == "vm"
& !word == "vmg"
& !word == "vrpg"
& !word == "vst"
& !word == "read"
& !word == "news"
& !word == "image"
& !word == "posts"
& !word == "jp"
& !word == "sci"
& !word == "vg"
& !word == "po"
& !word == "toy"
& !word == "vt"
& !word == "wg"
& !word == "biz"
& !word == "ck"
& !word == "desktop"
& !word == "enable"
& !word == "feedback"
& !word == "int"
& !word == "verification"
& !word == "respective"
& !word == "vr"
& !word == "wsg"
& !word == "aco"
& !word == "adv"
& !word == "delete"
& !word == "cm"
& !word == "disable"
& !word == "bfutababurichantomorrowphoton"
& !word == "cgl"
& !word == "comlen"
& !word == "cooldowns"
& !word == "copyrights"
& !word == "cssversion"
& !word == "diy"
& !word == "gd"
& !word == "hc"
& !word == "ic"
& !word == "incorrectreturn"
& !word == "jsversion"
& !word == "maxfilesize"
& !word == "maxlines"
& !word == "mlp"
& !word == "payment"
& !word == "postform"
& !word == "pw"
& !word == "qa"
& !word == "qst"
& !word == "recaptcha"
& !word == "refresh"
& !word == "replyreturn"
& !word == "soc"
& !word == "sp"
& !word == "trademarks"
& !word == "trv"
& !word == "uploaded"
& !word == "hm"
& !word == "xs"
& !word == "yotsubayotsuba"
& !word == "boards"
& !word == "faq"
& !word == "announcementcrypto"
& !word == "bolsheviknatonazihippiepiraterepublicantask"
& !word == "bypass"
& !word == "capitalistanarchistblack"
& !word == "flaggeographic"
& !word == "huggerunited"
& !word == "locationanarcho"
& !word == "login"
& !word == "nationalistconfederatecommunistcataloniademocrateuropeanfascistgadsdengayjihadikekistanimuslimnational"
& !word == "nationswhite"
& !word == "refreshpost"
& !word == "supremacistfileplease"
& !word == "ztemplartree"
& !word == "posters"
& !word == "wpjizlog"
& !word == "xxfbsv"
& !word == "wsr"
& !word == "mon"
& !word == "tue"
& !word == "wed"
& !word == "thu"
& !word == "fri"
& !word == "sat"
& !word == "sun"
& !word == "tues"
& !word == "emyqupza"
& !word == "nlcbzjyk"
& !word == "oq"
& !word == "avqxwczz"
& !word == "lot"
& !word == "day"
& !word == "guy"
& !word == "announcement"
& !word == "yeah"
& !word == "anon"
& !word == "watch"
& !grepl("[^A-Za-z]", word)
& !word == "[a-zA-Z]{,2}"
& word %in% GradyAugmented)
#below will replace a word with another word
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "niggers", "nigger")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "jews", "jew")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "jewish", "jew")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "woman", "women")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "fucked", "fuck")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "fucks", "fuck")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "fuckers", "fuck")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "fuckin", "fuck")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "fucker", "fuck")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "retards", "retard")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "retarded", "retard")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "retardation", "retard")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "campaings", "campaign")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "posted", "posting")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "shitskins", "nigger")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "shitskin", "nigger")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "christians", "christian")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "israeli", "israel")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "guy", "guys")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "whites", "white")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "indians", "indian")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "poos", "poo")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "parties", "party")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "kikes", "jew")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "kike", "jew")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "glow", "glowie")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "glowies", "glowie")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "glows", "glowie")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "glowfags", "glowie")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "glowshills", "glowie")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "guyss", "guy")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "guys", "guy")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "shills", "shill")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "shilling", "shill")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "shilled", "shill")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "europeans", "european")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "catholics", "catholic")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "masks", "mask")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "russians", "russian")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "indians", "indian")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "catholics", "catholic")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "chinks", "chinese")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "chink", "chinese")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "gook", "japanese")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "gooks", "japanese")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "woman", "women")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "parties", "party")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "governments", "government")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "american", "america")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "faggots", "faggot")
tidy_pol_fixed$word <- str_replace(tidy_pol_fixed$word, "campaigns", "campaign")
# Add date
date <- Sys.Date()
tidy_pol_fixed2 <- tidy_pol_fixed %>%
count(word, sort = TRUE) %>%
print(n = 20) %>%
cbind(date)
# Goodness of fit test ($\chi^2$). p-value < 0.05 = significant result that the
# observations are indepenent and vary in probabilities of showing up.
# top <- subset(tidy_pol_fixed2, tidy_pol_fixed2$n >= 200)
# chi <- chisq.test(top$n)
# options(scipen = 999) # removes scientific notation
# chi
# =========== Time to Visualize ===========
# Import CSV as tidy_pol_fixed2
#tidy_pol_fixed2 <- read.csv("~/Documents/Stats/4ChanScraper/csv/Aug 30 2023 18:00:58.csv")
#experimenting with removing top words
tidy_pol_fixed2 <- tidy_pol_fixed2 %>%
filter(!word == "jew"
& !word == "nigger"
& !word == "white"
& !word == "fuck"
& !word == "women"
& !word == "faggot"
& !word == "world"
& !word == "posting"
& !word == "retard"
& !word == "real"
& !word == "learn"
& !word == "party"
& !word == "retard"
& !word == "country"
& !word == "america"
& !word == "life")
tidy_pol_fixed2 %>%
top_n(50) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = n)) +
scale_fill_distiller( palette = "Greens") +
geom_col() +
labs(title = "Most Used Words",
x = "Words",
y = "Count",
fill = "Results") +
coord_flip() +
theme_dark(base_size = 12.5)
tidy_pol_fixed2 %>%
with(wordcloud(word, n, max.words = 150, random.order = FALSE, rot.per = 0.0,
colors = brewer.pal(8, "Dark2")))
# Sentiment analysis, and visuals with a bar grapoh.
# This will take a few minutes to process
# On my Ryzen 7 3700X, and 16Gb of RAM, it took about 5 minutes.
#sentiment_tidy_pol_fixed_2 <- get_nrc_sentiment(tidy_pol_fixed2$word)
# barplot(colSums(sentiment_tidy_pol_fixed_2),
# las = 2,
# col = rainbow(10),
# ylab = 'Count',
# main = 'Pol Sentiment Scores')
# Time to Save the Data
timestamp <- format(Sys.time(), "%b %d %Y %X")
filename <- paste0("~/Documents/Stats/4ChanScraper/",timestamp,".csv")
write.csv(tidy_pol_fixed2, file = filename)
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