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twitter_unsupervised_new.R
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# uploads ####
options(scipen = 999)
library(gdata)
# for crawling Twitter data
library(academictwitteR)
# library(rtweet)
#library(corpus)
library(quanteda)
library(udpipe)
library(stopwords)
library(corpustools)
library(quanteda.textstats)
library(dplyr)
library(tidyverse)
library(lubridate)
library(tokenizers)
library(tidytext)
library(stringi)
library(readtext)
library(parallel)
library(stringr)
library(widyr)
library(irlba)
library(furrr)
library(stm)
library(slider)
library(ggthemes)
library("stminsights")
library("gridExtra")
library("ggpubr")
library(readxl)
# "%nin%" <- Negate("%in%")
setwd("C:/Users/rocpa/OneDrive/Desktop/CNT/twitter_unsupervised/from_server/16_11_2022/")
folder <- "data_new/save_32/" # for output location
# Dataframe processing and dfm creation (lists for processing in keywords_cnt.xls) ####
load("df_20_22.Rdata")
df_20_22 <- df_20_22[df_20_22$selected_2 == 1,]
# df_20_22 <- rename(df_20_22, original_language = text)
# df_20_22 <- rename(df_20_22, text = en_text)
# save(df_20_22,file="df_20_22.Rdata")
# for cleaning and processing
df_20_22$text <- str_replace_all(df_20_22$text,"\\b's\\b","")
df_20_22$text <- str_replace_all(df_20_22$text,"\\b-\\b","_")
# German-specific processing
#key_de <- read.xls("keywords_cnt.xls",sheet = "lemma_de", encoding = "latin1")[,1]
key_de <- read_xls("keywords_cnt.xls",sheet = "lemma_de")[,1]
key_de <- paste0("\\b",key_de,"\\b")
#lemma_de <- read.xls("keywords_cnt.xls",sheet = "lemma_de", encoding="latin1" )[,2]
lemma_de <- read_xls("keywords_cnt.xls",sheet = "lemma_de")[,2]
lemma_de <- paste0("\\b",lemma_de,"\\b")
names(lemma_de) <- key_de
df_20_22[df_20_22$country == "Germany",]$text <- str_replace_all(df_20_22[df_20_22$country == "Germany",]$text,lemma_de)
# Italian-specific processing
# key_it <- read.xls("keywords_cnt.xls",sheet = "lemma_it", encoding = "latin1")[,1]
key_it <- read_xls("keywords_cnt.xls",sheet = "lemma_it")[,1]
key_it <- paste0("\\b",key_it,"\\b")
#lemma_it <- read.xls("keywords_cnt.xls",sheet = "lemma_it", encoding="latin1" )[,2]
lemma_it <- read_xls("keywords_cnt.xls",sheet = "lemma_it" )[,2]
lemma_it <- paste0("\\b",lemma_it,"\\b")
names(lemma_it) <- key_it
df_20_22[df_20_22$country == "Italy",]$text <- str_replace_all(df_20_22[df_20_22$country == "Italy",]$text,lemma_it)
# # English common terms
# eng <- read.xls("keywords_cnt.xls",sheet = "lemma_eng", encoding = "latin1")[,1]
eng <- read_xls("keywords_cnt.xls",sheet = "lemma_eng")[,1]
eng <- paste0("\\b",eng,"\\b")
# lemma_eng <- read.xls("keywords_cnt.xls",sheet = "lemma_eng", encoding="latin1" )[,2]
lemma_eng <- read_xls("keywords_cnt.xls",sheet = "lemma_eng")[,2]
lemma_eng <- paste0("\\b",lemma_eng,"\\b")
names(lemma_eng) <- eng
df_20_22$text <- str_replace_all(df_20_22$text,lemma_eng)
# compounds
#cmpd <- read.xls("keywords_cnt.xls",sheet = "compound_dfm", encoding = "latin1")[,1]
cmpd <- read_xls("keywords_cnt.xls",sheet = "compound_dfm")[,1]
cmpd <- paste0("\\b",cmpd,"\\b")
#cmpdlinked <- read.xls("keywords_cnt.xls",sheet = "compound_dfm", encoding="latin1" )[,2]
cmpdlinked <- read_xls("keywords_cnt.xls",sheet = "compound_dfm")[,2]
cmpdlinked <- paste0("\\b",cmpdlinked,"\\b")
names(cmpdlinked) <- cmpd
df_20_22$text <- str_replace_all(df_20_22$text,cmpdlinked)
#
# dfdelete <- read.xls("keywords_cnt.xls",sheet = "usernamedelete", encoding = "latin1")
dfdelete <- read_xls("keywords_cnt.xls",sheet = "usernamedelete")
for (i in dfdelete$usrnm) {
p <- dfdelete[dfdelete$usrnm == i,]$word
p <- paste0("\\b",p,"\\b")
d <- rep(" ",length(p))
names(d) <- p
df_20_22[df_20_22$user_username == i,]$text <- str_replace_all(df_20_22[df_20_22$user_username == i,]$text,d)
}
df_20_22$text <- str_squish(df_20_22$text)
#
df_20_22$monthyear <- format(as.Date(df_20_22$created_at),'%m-%Y')
df_20_22$monthyear <- factor(df_20_22$monthyear, levels = c("01-2020","02-2020","03-2020","04-2020","05-2020","06-2020",
"07-2020","08-2020","09-2020","10-2020","11-2020",
"12-2020","01-2021","02-2021","03-2021","04-2021",
"05-2021","06-2021","07-2021", "08-2021","09-2021",
"10-2021","11-2021", "12-2021","01-2022","02-2022",
"03-2022","04-2022","05-2022","06-2022","07-2022",
"08-2022","09-2022", "10-2022","11-2022","12-2022"))
df_20_22$trimester <- "xxx"
df_20_22[df_20_22$monthyear == "01-2020",]$trimester <- "01-2020"
df_20_22[df_20_22$monthyear == "02-2020",]$trimester <- "01-2020"
df_20_22[df_20_22$monthyear == "03-2020",]$trimester <- "01-2020"
df_20_22[df_20_22$monthyear == "04-2020",]$trimester <- "02-2020"
df_20_22[df_20_22$monthyear == "05-2020",]$trimester <- "02-2020"
df_20_22[df_20_22$monthyear == "06-2020",]$trimester <- "02-2020"
df_20_22[df_20_22$monthyear == "07-2020",]$trimester <- "03-2020"
df_20_22[df_20_22$monthyear == "08-2020",]$trimester <- "03-2020"
df_20_22[df_20_22$monthyear == "09-2020",]$trimester <- "03-2020"
df_20_22[df_20_22$monthyear == "10-2020",]$trimester <- "04-2020"
df_20_22[df_20_22$monthyear == "11-2020",]$trimester <- "04-2020"
df_20_22[df_20_22$monthyear == "12-2020",]$trimester <- "04-2020"
df_20_22[df_20_22$monthyear == "01-2021",]$trimester <- "01-2021"
df_20_22[df_20_22$monthyear == "02-2021",]$trimester <- "01-2021"
df_20_22[df_20_22$monthyear == "03-2021",]$trimester <- "01-2021"
df_20_22[df_20_22$monthyear == "04-2021",]$trimester <- "02-2021"
df_20_22[df_20_22$monthyear == "05-2021",]$trimester <- "02-2021"
df_20_22[df_20_22$monthyear == "06-2021",]$trimester <- "02-2021"
df_20_22[df_20_22$monthyear == "07-2021",]$trimester <- "03-2021"
df_20_22[df_20_22$monthyear == "08-2021",]$trimester <- "03-2021"
df_20_22[df_20_22$monthyear == "09-2021",]$trimester <- "03-2021"
df_20_22[df_20_22$monthyear == "10-2021",]$trimester <- "04-2021"
df_20_22[df_20_22$monthyear == "11-2021",]$trimester <- "04-2021"
df_20_22[df_20_22$monthyear == "12-2021",]$trimester <- "04-2021"
df_20_22[df_20_22$monthyear == "01-2022",]$trimester <- "01-2022"
df_20_22[df_20_22$monthyear == "02-2022",]$trimester <- "01-2022"
df_20_22[df_20_22$monthyear == "03-2022",]$trimester <- "01-2022"
df_20_22[df_20_22$monthyear == "04-2022",]$trimester <- "02-2022"
df_20_22[df_20_22$monthyear == "05-2022",]$trimester <- "02-2022"
df_20_22[df_20_22$monthyear == "06-2022",]$trimester <- "02-2022"
df_20_22[df_20_22$monthyear == "07-2022" ,]$trimester <- "03-2022"
df_20_22[df_20_22$monthyear == "08-2022",]$trimester <- "03-2022"
df_20_22[df_20_22$monthyear == "09-2022",]$trimester <- "03-2022"
df_20_22[df_20_22$monthyear == "10-2022",]$trimester <- "04-2022"
df_20_22[df_20_22$monthyear == "11-2022",]$trimester <- "04-2022"
df_20_22[df_20_22$monthyear == "12-2022",]$trimester <- "04-2022"
#
df_20_22$trimester <- factor(df_20_22$trimester, levels= c("01-2020","02-2020","03-2020","04-2020",
"01-2021","02-2021","03-2021","04-2021",
"01-2022","02-2022","03-2022","04-2022"))
df_20_22 <- df_20_22 %>% filter(! monthyear %in% c("01-2020","02-2020")) # one only for each month and not strict covid
# remove words
# rem_dfm <- read.xls("keywords_cnt.xls",sheet = "rem_dfm", encoding = "latin1")[,1]
rem_dfm <- read_xls("keywords_cnt.xls",sheet = "rem_dfm")[,1]
rem_dfm <- unique(rem_dfm)
rem_char <- c("http*","@*","€","+","|","s","faq","=","_","__","~","___")
# List of economic keywords to plot apart or not plot
de_policies <- c(
"corona_aid",
"corona_emergency_aid",
"economic_stabilization_fund",
"stimulus_package",
"social_protection_package",
"social_protection_package_2",
"social_protection_package_3",
"bridging_aid",
"corona_tax_assistance",
"bridging_aid_2",
"november_aid",
"december_aid",
"november_december_aid",
"bridging_aid_3",
"new_start_aid",
"bridging_aid_3_plus",
"bridging_aid_4",
"new_start_aid_plus",
"new_start_aid22",
"recoveryplan",
"digital_pact",
"nextgeneration",
"nextgenerationeu",
"darp",
"reacteu",
"esf"
)
it_policies <- c(
"dc_aiuti",
"dc_cureitaly",
"dc_liquidity",
"dc_relaunch",
"dc_august",
"dc_reliefs",
"dc_reliefs_2",
"dc_reliefs_3",
"dc_reliefs_4",
"dc_supports",
"dc_supports_2",
"dc_supports_3",
"nextgeneration",
"nextgenerationeu",
"recoveryplan",
"pnrr",
"reacteu",
"esf",
"pnrr_resources",
"pnrr_funds"
)
# For text detection: bigram, trigrams and word extraction ####
bigrams <- read.csv("bigrams_4.csv",sep=";")
trigrams_can <- trigrams %>% filter(str_starts(trigram,"can"))
write.csv(bigrams_be,file="bigrams_be.csv",row.names = F)
bigrams_tx <- df_20_22[,-4] %>% filter(country == "Italy") %>% unnest_tokens(bigram, text, token = "ngrams", n = 2)
bigrams_tx %>% dplyr::count(bigram, sort = TRUE)
bigrams_separate <- bigrams_tx %>% separate(bigram,c("word1","word2"),sep=" ")
bigrams_filtered <- bigrams_separate %>%
filter(!word1 %in% stopwords_en) %>%
filter(!word2 %in% stopwords_en)
bigrams_filtered <- bigrams_filtered %>% dplyr::count(word1, word2, sort = TRUE)
bigrams_united <- bigrams_filtered %>% unite(bigram, word1, word2, sep = " ")
# bigrams_united <- bigrams_united$bigram
bigrams_united <- unique(bigrams_united)
write.csv(bigrams_united,"bigrams_it.csv",row.names= F)
trigrams_tx <- df_20_22[,-4] %>% filter(country == "Italy") %>% unnest_tokens(trigram, text, token = "ngrams", n = 3)
trigrams_tx %>% dplyr::count(trigram, sort = TRUE)
trigrams_separate <- trigrams_tx %>% separate(trigram,c("word1","word2","word3"),sep=" ")
trigrams_filtered <- trigrams_separate %>%
filter(!word1 %in% stopwords_en) %>%
filter(!word2 %in% stopwords_en) %>%
filter(!word3 %in% stopwords_en)
trigrams_filtered <- trigrams_filtered %>% dplyr::count(word1, word2,word3, sort = TRUE)
trigrams_united <- trigrams_filtered %>% unite(trigram, word1, word2,word3, sep = " ")
# trigrams_united <- trigrams_united$trigram
trigrams_united <- unique(trigrams_united)
#
write.csv(trigrams_united,"trigrams_it.csv",row.names= F)
quadrigrams_tx <- df_20_22[,-4] %>% filter(country == "Italy") %>% unnest_tokens(quadrigram, text, token = "ngrams", n = 4)
quadrigrams_tx %>% dplyr::count(quadrigram, sort = TRUE)
quadrigrams_separate <- quadrigrams_tx %>% separate(quadrigram,c("word1","word2","word3","word4"),sep=" ")
quadrigrams_filtered <- quadrigrams_separate %>%
filter(!word1 %in% stopwords_en) %>%
filter(word2 == "and") %>%
filter(!word3 %in% stopwords_en) %>%
filter(!word4 %in% stopwords_en)
quadrigrams_filtered <- quadrigrams_filtered %>% dplyr::count(word1, word2,word3,word4, sort = TRUE)
quadrigrams_united <- quadrigrams_filtered %>% unite(quadrigrams, word1, word2,word3,word4, sep = " ")
write.csv(quadrigrams_united,"quadrigrams_it.csv",row.names= F)
ngram_it <- read.csv("trigrams_it.csv",sep =",")# %>% filter(n >= 4)
ngram_de <- read.csv("trigrams_de.csv",sep = ",") # %>% filter(n >= 4)
ngram_tot <- rbind(ngram_it,ngram_de)
ngram_totqr <- ngram_tot %>% filter(duplicated(quadrigrams))
ngram_de <- ngram_de %>% filter(trigram %nin% ngram_it$trigram)
write.csv(ngram_de,file="trigrams_de_only.csv")
check <- ngram %>% filter(n >= 3) %>% filter(bigram %nin% cmpd)
tri_it <- read.csv("trigrams_it_only.csv",sep =",")
qua <- qua %>% filter(quadrigram %nin% cmpd)
## unique term
unique(stringr::str_extract_all(df_20_22[df_20_22$selected_2 == 1,]$text,
"\\b[:alnum:]*\\-?[:alnum:]*\\-?[:alnum:] nextgeneration_eu\\-?[:alnum:]*\\-?[:alnum:]*\\b"))
wordouble <- unique(stringr::str_extract_all(df_20_22$text,
"\\b\\-?[:alnum:]* \\-?[:alnum:]* extended\\b"))
wordouble <- unlist(wordouble)
wordouble <- unique(wordouble)
wordouble
write.csv(wordouble,file="president_1.csv",row.names = F)
# filter dataframe
txt <- df_20_22 %>% filter(str_detect(text,"\\bstimulus_package\\b") & str_detect(text,"\\bclimate_protection\\b"))
tstcheck <- df_20_22 %>% filter(str_detect(original_language,regex("cigo")))
tstcheck$text
# check feature in dfm
textfreq <- textstat_frequency(dfm_it_en)
textstat_frequency(dfm_it_en) %>% subset(feature %in% "bike_paths")
# Document-feature matrix ####
# dfm_tot <- tokens(corpus(df_20_22),
# remove_punct = TRUE, remove_numbers = TRUE,remove_url = TRUE) %>%
# tokens_remove(c("http*","@*","€","+","|","s","faq","=","für","von","l","s","il","la","di",stopwords("en"),stopwords_en,
# rem_dfm)) %>%
# dfm()
#
# dfm_tot[1,]$ID
# dfm_tot[dfm_tot$ID == "MISE_GOV 483703",]
#
# tmod_wf <- textmodel_wordfish(dfm_tot, dir = c(4051, 309))
## German dfm
dfm_de_en <- tokens(corpus(df_20_22[df_20_22$country == "Germany",]),
remove_punct = TRUE, remove_numbers = TRUE,remove_url = TRUE) %>%
tokens_remove(c("für","von",stopwords("en"),stopwords_en,
rem_dfm, rem_char)) %>%
dfm()
dfm_de_en
topfeatures(dfm_de_en,50)
save(dfm_de_en,file=paste0(folder,"DE/dfm_de_en.Rdata"))
#
# dfm_de_enWS <- tokens(df2022scoresDE,
# remove_punct = TRUE, remove_numbers = TRUE,remove_url = TRUE) %>%
# tokens_remove(c("für","von",stopwords("en"),stopwords_en,
# rem_dfm, rem_char)) %>%
# dfm()
#
# save(dfm_de_enWS,file="data_new/save_29/DE/dfm_de_enWS.Rdata")
## Italian dfm
dfm_it_en <- tokens(corpus(df_20_22[df_20_22$country == "Italy",]),
remove_punct = TRUE, remove_numbers = TRUE,remove_url = TRUE) %>%
tokens_remove(c("l","il","la","di",stopwords("en"),stopwords_en,
rem_dfm, rem_char)) %>%
dfm()
dfm_it_en
topfeatures(dfm_it_en,50)
save(dfm_it_en,file=paste0(folder,"IT/dfm_it_en.Rdata"))
# Results: sample ####
library(forcats)
deall <- df_20_22 %>% filter(country == "Germany") %>%
mutate(actor = recode(actor,
"POL" = "Political\nActors",
"TA" = "Trade\nAssociations",
"TU" = "Trade\nUnions")) %>%
ggplot(aes(x = fct_rev(fct_infreq(actor)), fill = actor)) + geom_bar() +
geom_text(aes(label = ..count..), stat = "count", vjust = 0,hjust = 1, size = 5) +
xlab("count tweets") +
coord_flip() +
facet_wrap(~ country, scales = "free", nrow = 2) +
scale_fill_manual(values = c("Political\nActors" = "red","Trade\nAssociations" = "limegreen",
"Trade\nUnions" = "purple")) +
guides(fill="none") +
theme_bw() +
theme(strip.background = element_rect(fill="beige"),
axis.title.y = element_blank(),
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size =10),
strip.text.x = element_text(size = 12)
)
detime <- df_20_22 %>% filter(country == "Germany") %>%
mutate(actor = recode(actor,
"POL" = "Political Actors",
"TA" = "Trade Associations",
"TU" = "Trade Unions")) %>%
ggplot(aes(x = monthyear, fill = actor)) +
geom_bar(position = position_dodge()) +
# geom_text(aes(label = ..count..), stat = "count", vjust = 0) +
# coord_flip() +
scale_x_discrete(breaks = c("01-2020", "06-2020","01-2021","06-2021","01-2021","06-2021","01-2022","06-2022","12-2022")) +
xlab("date") +
# facet_wrap(~ country, scales = "free", nrow = 2) +
# facet_wrap(~ segment , scales = "free") +
scale_fill_manual(values = c("Political Actors" = "red","Trade Associations" = "limegreen",
"Trade Unions" = "purple")) +
guides(fill="none") +
theme_bw() +
theme(axis.title.y = element_blank(),
plot.margin = unit(c(0.2,0.3,0.2,0.2), "cm"),
axis.text.x = element_text(size = 10))
itall <- df_20_22 %>% filter(country == "Italy") %>%
mutate(actor = recode(actor,
"POL" = "Political\nActors",
"TA" = "Trade\nAssociations",
"TU" = "Trade\nUnions")) %>%
ggplot(aes(x = fct_rev(fct_infreq(actor)), fill = actor)) + geom_bar() +
geom_text(aes(label = ..count..), stat = "count", vjust = 0,hjust = 1, size = 5) +
xlab("count tweets") +
coord_flip() +
facet_wrap(~ country, scales = "free", nrow = 2) +
scale_fill_manual(values = c("Political\nActors" = "red","Trade\nAssociations" = "limegreen",
"Trade\nUnions" = "purple")) +
guides(fill="none") +
theme_bw() +
theme(strip.background = element_rect(fill="beige"),
axis.title.y = element_blank(),
axis.text.y = element_text(size = 12),
axis.text.x = element_text(size =10),
strip.text.x = element_text(size = 12)
)
ittime <- df_20_22 %>% filter(country == "Italy") %>%
mutate(actor = recode(actor,
"POL" = "Political Actors",
"TA" = "Trade Associations",
"TU" = "Trade Unions")) %>%
ggplot(aes(x = monthyear, fill = actor)) +
geom_bar(position = position_dodge()) +
# geom_text(aes(label = ..count..), stat = "count", vjust = 0) +
# coord_flip() +
scale_x_discrete(breaks = c("01-2020", "06-2020","01-2021","06-2021","01-2021","06-2021","01-2022","06-2022","12-2022")) +
xlab("date") +
# facet_wrap(~ country, scales = "free", nrow = 2) +
# facet_wrap(~ segment , scales = "free") +
scale_fill_manual(values = c("Political Actors" = "red","Trade Associations" = "limegreen",
"Trade Unions" = "purple")) +
guides(fill="none") +
theme_bw() +
theme(axis.title.y = element_blank(),
plot.margin = unit(c(0.2,0.3,0.2,0.2), "cm"),
axis.text.x = element_text(size = 10))
ggpubr::ggarrange(deall,detime,itall,ittime,widths = c(0.5, 1))
ggsave(paste0(folder,"figures/sampleNEW.jpg"),width = 13, height = 6)
# Results: tf-idf ####
library(stopwords)
rem_it <- read_xls("keywords_cnt.xls",sheet = "rem_it")[,1]
rem_de <- read_xls("keywords_cnt.xls",sheet = "rem_de")[,1]
df <- df_20_22 %>% unnest_tokens(word, text, token = stringr::str_split, pattern = " ")
df$word <- str_remove_all(df$word, "[^#_[:^punct:]]")
# Germany
dfDE <- df %>% filter(country == "Germany") %>% filter(!word %in% stop_words$word,
!word %in% stopwords("en"),
# !word %in% stopwords_en,
!word %in% rem_dfm$remove_list,
!word %in% rem_de$rem_de)
dfDE <- replace(dfDE, dfDE =='', NA)
dfDE <- dfDE %>% drop_na(word)
dfDE <- dfDE %>% # filter(country == "Germany") %>%
filter(!word %in% c(it_policies,de_policies)) %>%
count(country,actor, word,sort = TRUE)
total_wordsDE <- dfDE %>%
group_by(country,actor) %>%
# group_by(actor) %>%
summarize(total = sum(n))
dfDE <- left_join(dfDE, total_wordsDE)
dfDE$segment <- paste0(dfDE$country,"_",dfDE$actor)
df_tf_idfDE <- dfDE %>% bind_tf_idf(word,segment, n)
# Italy
dfIT <- df %>% filter(country == "Italy") %>% filter(!word %in% stop_words$word,
!word %in% stopwords("en"),
# !word %in% stopwords_en,
!word %in% rem_dfm$remove_list,
!word %in% rem_it$rem_it)
dfIT <- replace(dfIT, dfIT =='', NA)
dfIT <- dfIT %>% drop_na(word)
dfIT <- dfIT %>% # filter(country == "Germany") %>%
filter(!word %in% c(it_policies,de_policies)) %>%
count(country,actor, word,sort = TRUE)
total_wordsIT <- dfIT %>%
group_by(country,actor) %>%
# group_by(actor) %>%
summarize(total = sum(n))
dfIT <- left_join(dfIT, total_wordsIT)
dfIT$segment <- paste0(dfIT$country,"_",dfIT$actor)
df_tf_idfIT <- dfIT %>% bind_tf_idf(word,segment, n)
# df_tf_idfDE %>% arrange(desc(tf_idf))
#df_tf_idfDE <- df_tf_idf
#df_tf_idfIT <- df_tf_idf
df_tf_idf <- rbind(df_tf_idfDE,df_tf_idfIT)
df_tf_idf <- df_tf_idf %>% mutate(actor = recode(actor,
"POL" = "Political Actors",
"TA" = "Trade Associations",
"TU" = "Trade Unions")) %>%
mutate(segment = recode(
segment,
"Germany_POL" = "Germany\nPolitical Actors",
"Italy_POL" = "Italy\nPolitical Actors",
"Germany_TA" = "Germany\nTrade Associations",
"Italy_TA" = "Italy\nTrade Associations",
"Germany_TU" = "Germany\nTrade Unions",
"Italy_TU" = "Italy\nTrade Unions",
))
#df_tf_idf %>% # filter(country == "Italy") %>%
df_tf_idf %>%
# group_by(country, actor) %>%
group_by(segment) %>%
slice_max(tf_idf, n = 10) %>%
# group_by(country,actor,tf_idf) %>%
filter(! word %in% c(it_policies,de_policies,rem_dfm,rem_char,
"corona_bonus","corona_special_payment")) %>%
ungroup() %>%
ggplot(aes(x = reorder(word,tf_idf),y = tf_idf, fill = actor)) +
geom_col() +
coord_flip() +
scale_y_continuous(label = scales::percent) +
# facet_wrap(~ country + actor, scales = "free") +
# facet_wrap(~ factor(country,c("Germany","Italy")) +
# factor(actor,c("Political Actors","Trade Associations","Trade Unions")), scales = "free", nrow = 3) +
facet_wrap(~ factor(segment,c("Germany\nPolitical Actors","Italy\nPolitical Actors",
"Germany\nTrade Associations","Italy\nTrade Associations",
"Germany\nTrade Unions","Italy\nTrade Unions")),
scales = "free", nrow = 3) +
scale_fill_manual(values = c("Political Actors" = "red","Trade Associations" = "limegreen",
"Trade Unions" = "purple")) +
theme_light() +
theme(strip.background = element_rect(fill="beige"),
strip.text = element_text(color = "black",size = 13),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_text(size=13),
axis.text.x = element_text(size=11)) +
guides(fill = FALSE)
ggsave(file=paste0(folder,"figures/tfidf.jpg"),width = 10, height = 10.5)
# Results: Topic extraction ####
load(paste0(folder,"IT/stm_it25.Rdata"))
load(paste0(folder,"IT/dfm_it_en.Rdata"))
stm_df_it <- quanteda::convert(dfm_it_en,to = "stm")
#
load(paste0(folder,"DE/stm_de25.Rdata"))
load(paste0(folder,"DE/dfm_de_en.Rdata"))
stm_df_de <- quanteda::convert(dfm_de_en,to = "stm")
# selection used later for adapting code to Germany or Italy
numm <- 25
stm_m <- stm_it25
stm_df <- stm_df_it
titleplot <- "Italy"
dfb <- df_20_22[df_20_22$country == titleplot & df_20_22$selected_2 == 1,]
# Results: summary ####
sg <- sageLabels(stm_m,10)
sg_prob <- tibble(topic = 1:numm,sg$marginal$prob) # marginal probability
sg_frex <- tibble(topic = 1:numm,sg$marginal$frex) # marginal frex
sg_prob_pol <- tibble(topic = 1:numm,sg$covnames[[1]],sg$cov.betas[[1]]$problabels) # POL prob
sg_prob_ta <- tibble(topic = 1:numm,sg$covnames[[2]],sg$cov.betas[[2]]$problabels) # TA probl
sg_prob_tu <- tibble(topic = 1:numm,sg$covnames[[3]],sg$cov.betas[[3]]$problabels) # TU prob
sg_frex_pol <- tibble(topic = 1:numm,sg$covnames[[1]],sg$cov.betas[[1]]$frexlabels) # POL frex
sg_frex_ta <- tibble(topic = 1:numm,sg$covnames[[2]],sg$cov.betas[[2]]$frexlabels) # TA frex
sg_frex_tu <- tibble(topic = 1:numm,sg$covnames[[3]],sg$cov.betas[[3]]$frexlabels) # TU frex
thoughts <- list()
for (i in 1:numm){ #
# thg_det <- findThoughts(stm_m, texts = df_de$text,n = 3, topics =i)$docs[[1]]
thought_text = list()
dates <- findThoughts(stm_m, texts = dfb$text,n = 20, topics =i)$index[[1]] #
for (n in dates) {
txx <- print(c(paste0(" ACT: ", dfb[n,]$actor,
" ACTsj: ", dfb[n,]$user_username," TXT: ",dfb[n,]$text," DATE: ", dfb[n,]$created_at,
" TWID: ",dfb[n,]$tweet_id," CNID ",dfb[n,]$conversation_id)))
thought_text[[n]] <- txx
thought_textfin <- do.call(rbind.data.frame, thought_text)
}
thoughts[[i]] <- thought_textfin
}
bind_rows(thoughts)
thoughts <- do.call(rbind.data.frame, lapply(thoughts,'[[',1))
colnames(thoughts) = c("1", "2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19","20") # columns where texts go
thoughts <- cbind(topic = 1:numm,thoughts)
# combining final report pieces and write excel
long_report <- cbind(sg_prob,sg_frex,sg_prob_pol,sg_prob_ta,sg_prob_tu,sg_frex_pol,sg_frex_ta,sg_frex_tu,thoughts)
# long_report <- cbind(sg_prob,sg_frex,thoughts)
write.csv(long_report,file=paste0(folder,"IT/",titleplot,numm,".csv"),row.names = F, col.names=T, sep=";", fileEncoding = "UTF-8")
# Results: Topic Proportion global ####
td_gamma <- tidy(stm_m, matrix = "gamma")
ID_row <- names(stm_df$documents) # the name of documents gets lost, the row number is reported
td_gamma <- cbind(td_gamma,ID_row) # Here I map each document to its name via row, I checked with content, it works
td_gamma <- cbind(td_gamma,dfb) # merge the gamma matrix with the dataframe via ID, so the variables to sort documents can be used
top_terms <- tidy(stm_m) %>%
group_by(topic,term) %>%
summarise(beta = mean(beta)) %>%
arrange(beta) %>%
top_n(5, beta) %>% #8
arrange(-beta)%>%
select(topic, term) %>%
summarise(terms = list(unique(term))) %>%
mutate(terms = map(terms, paste, collapse = ", ")) %>%
unnest()
gamma_terms <- td_gamma %>%
group_by(topic) %>%
summarise(gamma = mean(gamma)) %>%
arrange(desc(gamma)) %>%
left_join(top_terms, by = "topic")
gamma_terms$country <- titleplot
gamma_terms <- gamma_terms[order(-gamma_terms$gamma),]
gamma_terms$rank <- 1:25
IT_gammaterms <- gamma_terms
# Adding label and transformative/restorative policy
IT_gammaterms <- IT_gammaterms %>% mutate(label = recode(topic,
"1" = "1 Dc\nSupports", # brought cisl
"2" = "2 Pnrr\nInvestments" , #, "transitions",
"3" = "3 Funds\nAllocations", #*
"4" = "4 Dc\nRelaunch", # "life\nquality",
"5" = "5 Emergency\nCompany" , #"emergency\naid", # companies
"6" = "6 Liquidity",
"7" = "7 Agribusiness", # "agribusiness" , # "dc\naugust",
"8" = "8 Tourism", # agorarai
"9" = "9 Social\nConfrontations",
"10" = "10 European\nFunding",
"11" = "11 National\nRecovery" , # "sustainability",
"12" = "12 Investments\nBoost", # confederal_secretary, front_page
"13" = "13 Sustainable\nGrowth", # # fenealuil #spoke
"14" = "14 Pa\nSimplification",
"15" = "15 Pnrr\nImplementation", # confsal check all
"16" = "16 Funds\nAnnouncement", #?governance?
"17" = "17 Recoveryplan" , # "restart\ndecree",
"18" = "18 Contracts",
"19" = "19 Employment\nEquality", #*
"20" = "20 South", # credit
"21" = "21 Funding\nAccess",
"22" = "22 Public\nAdministration",
"23" = "23 Actors", #infrastructure
"24" = "24 Recovery\nInterventions", # thousand
"25" = "25 Pnrr" )) #"industry"))
IT_gammaterms <- IT_gammaterms %>% mutate(policy = recode(topic,
"1" = "restorative", # brought cisl
"2" = "transformative" , #, "transitions",
"3" = "transformative", #*
"4" = "restorative", # "life\nquality",
"5" = "restorative" , #"emergency\naid", # companies
"6" = "restorative",
"7" = "generic", # "agribusiness" , # "dc\naugust",
"8" = "restorative", # agorarai
"9" = "generic",
"10" = "transformative",
"11" = "transformative" , # "sustainability",
"12" = "generic", # confederal_secretary, front_page
"13" = "transformative", # # fenealuil #spoke
"14" = "transformative",
"15" = "transformative", # confsal check all
"16" = "generic", #?governance?
"17" = "transformative" , # "restart\ndecree",
"18" = "generic",
"19" = "transformative", #*
"20" = "transformative", # credit
"21" = "generic",
"22" = "generic",
"23" = "generic", #infrastructure
"24" = "restorative", # thousand
"25" = "generic" )) #"industry"))
IT_gammaterms <- IT_gammaterms %>% mutate(labelcol = recode(topic,
"5" = 5,
"10" = 10,
"2" = 2,
"15" = 15,
"20" = 20,
"9" = 9,
"24" = 24,
"19" = 19))
IT_gammaterms["labelcol"][is.na(IT_gammaterms["labelcol"])] <- 0
save(IT_gammaterms, file = paste0(folder,"IT/IT_gammaterms.Rdata"))
DE_gammaterms <- DE_gammaterms %>% mutate(label = recode(topic,
"1" = "1 Bridging\nAid", # intended consequences
"2" = "2 Stimulus\nPackage", # "stimulus\package", # "growth",
"3" = "3 Families", # "families", #*
"4" = "4 Work\nSecurity",
"5" = "5 Corona\nAid" , # "business", minimum short_time_allowance
"6" = "6 Electricity\nPrice", # electricity\nprice
"7" = "7 Extension\nAid",
"8" = "8 Retail\nSector", # "Companies\nRecovery
"9" = "9 Economic\nBoost", # Sustainable Goals
"10" = "10 Companies\nRecovery",
"11" = "11 European\nFunding",
"12" = "12 Climate\nProtection", # Families
"13" = "13 Digitalization",
"14" = "14 Bridging\nAid3",
"15" = "15 Basic\nSecurity", #creative industry, economic stabilisation_fund
"16" = "16 Special\nCorona Aid",
"17" = "17 Lockdown" , # "self\nemployed",
"18" = "18 Economic\nStabilisation",
"19" = "19 Wumms", # "recession",
"20" = "20 Social\nProtection" , #"lockdown",
"21" = "21 Debates",
"22" = "22 Germany\nin Eu",
"23" = "23 Startups",
"24" = "24 Gender\nEquality", # voluntary shopping, voluntary initiatives
"25" = "25 Tax\nEstimates"))
DE_gammaterms <- DE_gammaterms %>% mutate(policy = recode(topic,
"1" = "restorative", # brought cisl
"2" = "transformative" , #, "transitions",
"3" = "restorative", #*
"4" = "restorative", # "life\nquality",
"5" = "restorative" , #"emergency\naid", # companies
"6" = "restorative",
"7" = "restorative", # "agribusiness" , # "dc\naugust",
"8" = "restorative", # agorarai
"9" = "transformative",
"10" = "restorative",
"11" = "transformative" , # "sustainability",
"12" = "transformative", # confederal_secretary, front_page
"13" = "transformative", # # fenealuil #spoke
"14" = "restorative",
"15" = "transformative", # confsal check all
"16" = "restorative", #?governance?
"17" = "restorative" , # "restart\ndecree",
"18" = "restorative",
"19" = "restorative", #*
"20" = "restorative", # credit
"21" = "restorative",
"22" = "transformative",
"23" = "transformative", #infrastructure
"24" = "transformative", # thousand
"25" = "restorative" )) #"industry"))
DE_gammaterms <- DE_gammaterms %>% mutate(labelcol = recode(topic,
"2" = 2,
"13" = 13,
"20" = 20,
"19" = 19,
"5" = 5,
"9" = 9,
"8" = 8,
"24" = 24))
DE_gammaterms["labelcol"][is.na(DE_gammaterms["labelcol"])] <- 0
save(DE_gammaterms, file = paste0(folder,"DE/DE_gammaterms.Rdata"))
df <- rbind(DE_gammaterms,IT_gammaterms)
# Topic proportion national level
# Germany
#tpde <- # df %>% filter(country == "Germany") %>%
load(paste0(folder,"DE/DE_gammaterms.Rdata"))
tpde <- DE_gammaterms %>%
ggplot(aes(reorder(label,gamma), gamma, fill = as.factor(labelcol))) + #, fill = policy) +
# ggplot(aes(reorder(topic,gamma), gamma)) + #, fill = policy)) +
geom_col(color = "black") + #, width=.4
geom_text(aes(label = terms ), hjust = 0, y = 0.001, size = 9,# nudge_y = 0.00005, size = 5, # 0.0005
family = "IBMPlexSans") +
coord_flip() +
scale_y_continuous(expand = c(0,0),
limits = c(0, 0.10) ,
labels = scales::percent_format()) +
facet_wrap(~ country , scales = "free", dir = "v") +
# theme_tufte(base_family = "IBMPlexSans", ticks = FALSE) +
# labs(x = NULL, y = expression(gamma))
labs(x = NULL, y = "Topic Proportion") +
scale_fill_manual(values = c("2" = "salmon",
"13" = "green",
"20" = "lightblue",
"19" = "cyan",
"5" = "purple",
"9" = "brown2",
"8" = "lightgreen",
"24" = "tan1",
"0" = "white")) +
theme_bw() +
theme(axis.title.x = element_text(size = 22),
axis.text.x = element_text(size = 19),
axis.text.y = element_text(size = 24),
# legend.text = element_text(size=12),
strip.background = element_rect(fill="beige"),
strip.text.x = element_text(color = "black",size = 26),
plot.margin = unit(c(0.2, 0.37, 0.2, 0.2),
"inches")) +
guides(fill = "none")
save(tpde,file = paste0(folder,"DE/tpde.Rdata"))
# Italy
# tpit <- df %>% filter(country == "Italy") %>%
load(paste0(folder,"IT/IT_gammaterms.Rdata"))
tpit <- IT_gammaterms %>%
ggplot(aes(reorder(label,gamma), gamma, fill = as.factor(labelcol))) + #, fill = policy) +
geom_col(color = "black") + # , width=.4
geom_text(aes(label = terms ), hjust = 0, y = 0.001, size = 9,# nudge_y = 0.00005, size = 5, # 0.0005
family = "IBMPlexSans") +
coord_flip() +
scale_y_continuous(expand = c(0,0),
limits = c(0, 0.10) ,
labels = scales::percent_format()) +
facet_wrap(~ country , scales = "free", dir = "v") +
# theme(legend.position="bottom",
# axis.text.y = element_text(size = 20),
# legend.text = element_text(size=12),
# strip.background = element_rect(fill="beige"),
# strip.text = element_text(color = "black",size = 16)) +
# labs(x = NULL, y = expression(gamma))
labs(x = NULL, y = "Topic Proportion") +
scale_fill_manual(values = c("5" = "salmon",
"10" = "green",
"2" = "lightblue",
"15" = "cyan",
"20" = "purple",
"9" = "brown2",
"24" = "lightgreen",
"19" = "tan1",
"0" = "white")) +
theme_bw() +
theme(axis.title.x = element_text(size = 22),
axis.text.x = element_text(size = 19),
axis.text.y = element_text(size = 24),
# legend.text = element_text(size=12),
strip.background = element_rect(fill="beige"),
strip.text.x = element_text(color = "black",size = 26),
plot.margin = unit(c(0.2, 0.37, 0.2, 0.2),
"inches")) +
guides(fill = "none")
save(tpit,file = paste0(folder,"IT/tpit.Rdata"))
# # to prepare trimester-break visualization ####
#
# gamma_termsmy <- td_gamma %>%
# group_by(topic,trimester) %>%
# summarise(gamma = mean(gamma)) %>%
# arrange(desc(gamma))
#
# IT_gamma_termsmy <- gamma_termsmy %>% mutate(country = "Italy") %>%
# mutate(textlabel = topic)
#
# DE_gamma_termsmy <- gamma_termsmy %>% mutate(country = "Germany") %>%
# mutate(textlabel = topic)
#
# IT_gamma_termsmy <- IT_gamma_termsmy %>% mutate(label = recode(topic,
# "1" = "1 dc\nsupports", # brought cisl
# "2" = "2 pnrr\ninvestments" , #, "transitions",
# "3" = "3 funds\nallocations", #*
# "4" = "4 dc_relaunch", # "life\nquality",
# "5" = "5 emergency\ncompanies" , #"emergency\naid", # companies
# "6" = "6 liquidity",
# "7" = "7 agribusiness", # "agribusiness" , # "dc\naugust",
# "8" = "8 tourism", # agorarai
# "9" = "9 social\nconfrontations",
# "10" = "10 european\nfunding",
# "11" = "11 national\nrecovery" , # "sustainability",
# "12" = "12 active\npolicies", # confederal_secretary, front_page
# "13" = "13 sustainable\ngrowth", # # fenealuil #spoke
# "14" = "14 pa\nsimplification",
# "15" = "15 pnrr\nimplementation", # confsal check all
# "16" = "16 dc\nreliefs2", #?governance?
# "17" = "17 intervention\nuncertainty" , # "restart\ndecree",
# "18" = "18 contracts",
# "19" = "19 employment\nequity", #*
# "20" = "20 south", # credit
# "21" = "21 funding\naccess",
# "22" = "22 public\nadministration",
# "23" = "23 pnrr", #infrastructure
# "24" = "24 recovery\ninterventions", # thousand
# "25" = "25 workers" )) #"industry"))
#
# IT_gamma_termsmy <- IT_gamma_termsmy %>% mutate(policy = recode(topic,
# "1" = "restorative", # brought cisl
# "2" = "transformative" , #, "transitions",
# "3" = "transformative", #*
# "4" = "restorative", # "life\nquality",
# "5" = "restorative" , #"emergency\naid", # companies
# "6" = "restorative",
# "7" = "restorative", # "agribusiness" , # "dc\naugust",
# "8" = "restorative", # agorarai
# "9" = "transformative",
# "10" = "transformative",
# "11" = "restorative" , # "sustainability",
# "12" = "restorative", # confederal_secretary, front_page
# "13" = "transformative", # # fenealuil #spoke
# "14" = "transformative",
# "15" = "transformative", # confsal check all
# "16" = "restorative", #?governance?
# "17" = "restorative" , # "restart\ndecree",
# "18" = "restorative",
# "19" = "transformative", #*
# "20" = "transformative", # credit
# "21" = "restorative",
# "22" = "restorative",
# "23" = "transformative", #infrastructure
# "24" = "restorative", # thousand
# "25" = "restorative" )) #"industry"))
#
#
#
#
# DE_gamma_termsmy <- DE_gamma_termsmy %>% mutate(label = recode(topic,
# "1" = "1 bridging\naid", # intended consequences
# "2" = "2 stimulus_package", # "stimulus\package", # "growth",
# "3" = "3 families", # "families", #*
# "4" = "4 work\nsecurity",
# "5" = "5 corona\naid" , # "business", minimum short_time_allowance
# "6" = "6 electricity\nprice", # electricity\nprice
# "7" = "7 extension\naid",
# "8" = "8 retail\nsector", # "smes", # ver.di_chairman
# "9" = "9 economic\nboost",
# "10" = "10 schools",
# "11" = "11 european\nfunding",
# "12" = "12 climate\nprotection",
# "13" = "13 digitalization",
# "14" = "14 bridging\naid3",
# "15" = "15 basic\nsecurity", #creative industry, economic stabilisation_fund
# "16" = "16 special\ncorona aid",
# "17" = "17 lockdown" , # "self\nemployed",
# "18" = "18 economic\nstabilisation",
# "19" = "19 tax\nreduction", # "recession",
# "20" = "20 social\nprotection" , #"lockdown",
# "21" = "21 insurance\nprotection",
# "22" = "22 germany\nin eu",
# "23" = "23 wumms",
# "24" = "24 gender\nequality", # voluntary shopping, voluntary initiatives
# "25" = "25 national\neconomy"))
#
# DE_gamma_termsmy <- DE_gamma_termsmy %>% mutate(policy = recode(topic,
# "1" = "restorative", # brought cisl
# "2" = "transformative" , #, "transitions",
# "3" = "restorative", #*
# "4" = "restorative", # "life\nquality",
# "5" = "restorative" , #"emergency\naid", # companies
# "6" = "restorative",
# "7" = "restorative", # "agribusiness" , # "dc\naugust",
# "8" = "restorative", # agorarai
# "9" = "transformative",
# "10" = "restorative",
# "11" = "transformative" , # "sustainability",
# "12" = "transformative", # confederal_secretary, front_page
# "13" = "transformative", # # fenealuil #spoke
# "14" = "restorative",