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comment_cleaner.R
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comment_cleaner.R
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library(jsonlite)
library(sentimentr)
library(dplyr)
library(tidytext)
library(stopwords)
library(ggplot2)
library(stringr)
library(textdata)
library(topicmodels)
library(lubridate)
rm(list = ls())
memory.limit(size=56000)
###############
# Read Data #
###############
setwd('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/collected')
comments_all <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/collected/items_185_245.RDS')
str(comments_all)
# length: 5996184
abt <- read.csv("G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/ABT_base_full.csv", sep=',', header=T)
str(abt)
# select comments based on ABT's ID-s
comments_recent <- comments_all[comments_all$by %in% unique(abt$by), ]
comments_recent <- comments_all[comments_all$type == "comment", ]
###################
# Data Cleanings #
###################
# create date field
comments_recent$time <- as.Date(as.POSIXct(comments_recent$time, origin="1970-01-01"))
comments_recent$time <- floor_date(as_date(comments_recent$time), "month")
comments_recent <- comments_recent[comments_recent$time > "2019-08-01", ]
str(comments_recent)
# length: 2909608
###########################
# cleaning comment field #
###########################
comments_recent <- comments_recent[!is.na(comments_recent$text), ]
comments_recent$text <- gsub("'", "'", comments_recent$text)
# count of href than regex it
comments_recent$cnt_links <- str_count(comments_recent$text, pattern = "<a.*?/a>")
comments_recent$text <- gsub("<a.*?/a>", "", comments_recent$text)
# regex HTML tags like <p></p>
comments_recent$text <- gsub("<.*?>", "", comments_recent$text)
comments_recent$text <- gsub("\n", "'", comments_recent$text)
# count of qoutes than regex it
comments_recent$cnt_qout <- str_count(comments_recent$text, pattern = """)
comments_recent$text <- gsub(""", "", comments_recent$text)
comments_recent$text <- gsub("nâ\200\2", "e", comments_recent$text)
# regex other spams
comments_recent$text <- gsub(">", "'", comments_recent$text)
comments_recent$text <- gsub("/", " ", comments_recent$text)
# count of ! and ?
comments_recent$cnt_exclam <- str_count(comments_recent$text, pattern = "!")
comments_recent$cnt_ques <- str_count(comments_recent$text, pattern = '\\?')
# lowercasing the text:
comments_recent$text <- tolower(comments_recent$text)
# drop non alphanumerical characters
comments_recent$text <- gsub("'.*? ", " ", comments_recent$text)
comments_recent$text <- gsub("[^[:alnum:] ]", " ", comments_recent$text)
# drop numerical characters
comments_recent$text <- gsub("[0-9]", " ", comments_recent$text)
comments_recent$text <- gsub("’", "'", comments_recent$text)
# filter empty texts after regex
comments_recent <- comments_recent[comments_recent$text != "", ]
###########################################
# Sentiment analysis on the whole comment #
###########################################
# drop character after '
# before drop it run sentiment R sentiment analysis,
# on sentences:
comments1$sentiment <- sentiment(comments1$text)$sentiment
comments_recent$text <- gsub("'.*? ", " ", comments_recent$text)
#################
# Starting NLP #
#################
# tokenization
# stopword removal
comments_tokenized <- comments_recent %>%
unnest_tokens(word, text) %>%
anti_join(bind_rows(data.frame(word = stop_words$word),
data.frame(word = stopwords::stopwords(source = 'smart')),
data.frame(word = stopwords::stopwords(source = 'marimo')),
data.frame(word = stopwords::stopwords(source = 'snowball')),
data.frame(word = stopwords::stopwords(source = 'nltk')),
data.frame(word = c('yeah', 'gonna', 'uh', 'alright', 'um', 'lot', 'hey')))) %>%
group_by(id) %>%
summarize(text = str_c(word, collapse = " ")) %>%
ungroup()
comments_tokenized <- comments_tokenized %>%
left_join(select(comments_recent, by, id, parent, type, time, cnt_links, cnt_qout, cnt_exclam), by='id')
comments_tokenized <- comments_tokenized[!duplicated(comments_tokenized), ]
#################################
# Further feature engineering #
#################################
# length of a comment (just tokens without stopwords)
comments_tokenized$token_cnt <- str_count(comments_tokenized$text, '\\w+')
# create sophisticated index:
sop <- c()
for (i in 1:nrow(comments_tokenized)){
sop <- c(sop, length(unique(unlist(str_split(comments_tokenized$text[i], ' ')))) / length(unlist(str_split(comments_tokenized$text[i], ' '))))
}
comments_tokenized$sophis_index <- sop
###################################
# Group the data like ABT format #
###################################
ABT_comment <- comments_tokenized %>%
group_by(by, time) %>%
summarize(sum_qout = sum(cnt_qout),
sum_exclam = sum(cnt_exclam),
sum_link = sum(cnt_links),
cnt_comment = n(),
avg_token_len = mean(token_cnt))
str(ABT_comment)
saveRDS(ABT_comment, 'G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_month_user_agg.rds')
###############################################################
# Remaining data process when runnig this on the whole ABT #
###############################################################
"""
comments_recent1 <- comments_recent[1:1000000, ]
comments_recent2 <- comments_recent[1000001:2000000, ]
comments_recent3 <- comments_recent[2000001:2824962, ]
saveRDS(comments_recent1, file='G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comments_recent1.rds')
saveRDS(comments_recent2, file='G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comments_recent2.rds')
saveRDS(comments_recent3, file='G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comments_recent3.rds')
comments_recent1 <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comments_recent1.rds')
comments_recent2 <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comments_recent2.rds')
comments_recent3 <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comments_recent3.rds')
#comment_tokenized1 <- comments_tokenized
#saveRDS(comment_tokenized1, file='G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_tokenized1.rds')
#str(comment_tokenized1)
#comment_tokenized2 <- comments_tokenized
#saveRDS(comment_tokenized2, file='G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_tokenized2.rds')
#str(comment_tokenized2)
comment_tokenized3 <- comments_tokenized
saveRDS(comment_tokenized3, file='G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_tokenized3.rds')
str(comment_tokenized3)
comment_tokenized1 <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_tokenized1.rds')
comment_tokenized2 <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_tokenized2.rds')
comment_tokenized3 <- readRDS('G:/Saját meghajtó/HiFly/Common/Homokozó/nlp-hackathon/data/transformed/comment_tokenized3.rds')
df <- rbind(comment_tokenized1, comment_tokenized2, comment_tokenized3)
"""
###################################
# LDA #
# (not used because the huge ABT) #
###################################
# LDA on tokenized tibble:
# include only words that occur at least 50 times
set.seed(123)
word_sci_comment <- comments_tokenized %>%
group_by(id, time, word) %>%
summarize(word_total = n()) %>%
group_by(id, time) %>%
mutate(n = sum(word_total)) %>%
ungroup() %>%
filter(n > 20) %>%
sample_n(5000)
ggplot(word_sci_comment, aes(x=n)) + geom_histogram(binwidth = 5)
# convert into a document-term matrix
dtm <- word_sci_comment %>%
cast_dtm(id, word, word_total)
lda <- LDA(dtm, k = 7, control = list(seed= 1234))
str(lda)
lda@gamma
# add LDA flag to tibble
chapter_topics <- tidy(lda, matrix = "gamma")
chapter_topics %>%
arrange(document, gamma) %>% View()
mean(chapter_topics$gamma)
comments_recent <- comments_recent %>%
left_join(chapter_classifications, by=c('id'='document'))
# visualize LDA
top_terms <- chapter_topics %>%
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
arrange(topic, -beta)
top_terms %>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(term, beta, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip() +
scale_x_reordered()