data-raw/collect_tweets.R
In mcnakhaee/VaccinationinNLShiny: Vaccination in the Netherlands
## code to prepare `collect_news` dataset goes here
##-----------------------------------------------------------------------
library(tidyverse)
library(rtweet)
library(glue)
library(qdapRegex)
library(tidytext)
library(syuzhet)
library(lubridate)
date <- Sys.Date()
#Keywords Collect Tweets
##-----------------------------------------------------------------------
tweet_keywords <- c('vaccineren',
'vaccineren autism',
'vaccineren mmr',
'vaccineren measles',
'vaccineren corona',
'vaccineren coronavirus',
'vaccineren covid',
'vaccinatie autism',
'vaccinatie corona',
'Vaccinatie covid',
'Vaccinatie autism',
'vaxxhappened',
'CoronaVaccin',
'plandemic',
'autism vaccine',
'vrijheid vaccine',
'mmr vaccine',
'measles autism',
'Vaccinatie',
'antivax',
'vaccination autism',
'vaccination corona',
'antivax corona',
'antivax covid',
'coronavaccin',
'viruswaanzin',
'ikvaccineer',
'ikvaccineer',
'ikvaccineerniet',
'Gezondheidsraad',
'MyBodyMyChoice'
)
tweets_in_dutch <- data.frame()
tweets_in_news <- data.frame()
tweets_in_verified <- data.frame()
for (keyword in tweet_keywords) {
Sys.sleep(20)
tweet_tmp <- rtweet::search_tweets(keyword,n = 3000,retryonratelimit = TRUE,lang = "nl")
tweet_tmp$searched_keyword <- keyword
tweets_in_dutch <- bind_rows(tweets_in_dutch,tweet_tmp)
}
tweets_in_dutch %>%
save_as_csv(glue('../vaccinationinNL/tmp/tweets_in_dutch{date}','.csv'), prepend_ids = TRUE, na = "", fileEncoding = "UTF-8")
# preprocess tweets
##-------------------------------------------------------------------------------------------------------------------------------------
tweet_files <- list.files('../vaccinationinNL/tmp') %>%
.[str_detect(.,'tweets_in_dutch')]
tweet_files <- paste('../vaccinationinNL/tmp/',tweet_files,sep = '')
tweet_dfs <-purrr::map(tweet_files,rtweet::read_twitter_csv)
tweets_all <- data.frame()
for (df in tweet_dfs) {
tweets_all <- bind_rows(tweets_all,df)
}
## process anc clean text
filtered_tweets <- tweets_all %>%
distinct_all %>%
filter(str_detect(text,'vaccin') | str_detect(text,'Vaccin')| str_detect(text,'vax') | str_detect(text,'Vax')) %>%
mutate(text_cleaned = str_replace_all(text,"@\\w+", ""),
text_cleaned = str_replace_all(text_cleaned,"RT @[a-z,A-Z]*: ", ""),
text_cleaned = str_replace_all(text_cleaned,"http\\w+", ""),
text_cleaned = str_replace_all(text_cleaned,"#[a-z,A-Z]*", ""),
text_cleaned = rm_url(text_cleaned),
retweet_text_cleaned = str_replace_all(retweet_text,"@\\w+", ""),
retweet_text_cleaned = str_replace_all(retweet_text_cleaned,"RT @[a-z,A-Z]*: ", ""),
retweet_text_cleaned = str_replace_all(retweet_text_cleaned,"http\\w+", ""),
retweet_text_cleaned = str_replace_all(retweet_text_cleaned,"#[a-z,A-Z]*", ""),
retweet_text_cleaned = rm_url(retweet_text_cleaned),
quoted_text_cleaned = str_replace_all(quoted_text,"@\\w+", ""),
quoted_text_cleaned = str_replace_all(quoted_text_cleaned,"RT @[a-z,A-Z]*: ", ""),
quoted_text_cleaned = str_replace_all(quoted_text_cleaned,"http\\w+", ""),
quoted_text_cleaned = str_replace_all(quoted_text_cleaned,"#[a-z,A-Z]*", ""),
quoted_text_cleaned = rm_url(quoted_text_cleaned),
created_at = lubridate::ymd_hms(created_at),
quoted_created_at = lubridate::ymd_hms(quoted_created_at),
retweet_created_at = lubridate::ymd_hms(quoted_created_at),
by_day = lubridate::floor_date(created_at, "day"),
by_hour= lubridate::floor_date(created_at, "hour"),
by_day_quoted= lubridate::floor_date(quoted_created_at, "day"),
by_hour_quoted= lubridate::floor_date(quoted_created_at, "hour"),
by_day_retweet = lubridate::floor_date(retweet_created_at, "day"),
by_hour_retweet= lubridate::floor_date(retweet_created_at, "hour")
)
filtered_tweets%>%
select(screen_name,
text,
source,
mentions_screen_name,
retweet_screen_name,
quoted_screen_name,
created_at,
by_day,
by_hour,
by_day_retweet,
by_hour_retweet,
by_day_quoted,
by_hour_quoted,
hashtags,
is_retweet,
is_quote,
followers_count,
favorite_count,
retweet_count,
quote_count,
reply_count,
symbols,
location,
urls_url,
favourites_count,
friends_count,
retweet_text,
quoted_text,
verified,
text_cleaned,
retweet_text_cleaned,
quoted_text_cleaned) %>%
write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_v_{date}','.csv') )
#Sentiment Analysis
#---------------------------------------------------------------------------------
filtered_tweets <- read_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_v_{date}','.csv') )
###-------------------------------------------
filtered_tweets_sentiment_tweet_text <- filtered_tweets %>%
bind_cols(get_nrc_sentiment(as.vector(
filtered_tweets$text_cleaned),
language = 'dutch'))
filtered_tweets_sentiment_tweet_text %>%
write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_sentiment_tweet_text_v_{date}','.csv') )
### ------------------------------------
distinct_filtered_tweets <- filtered_tweets %>%
distinct(text,.keep_all = TRUE)
distinct_filtered_tweets_sentiment_text <- distinct_filtered_tweets %>%
bind_cols(get_nrc_sentiment(as.vector(
distinct_filtered_tweets$text_cleaned),
language = 'dutch'))
# distinct_tweets_sentiment_text%>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/distinct_tweets_sentiment_text_v_{date}','.csv') )
#------------------------
### Sentiment of retweets
#### --------------------
retweets <- filtered_tweets %>%
filter(is_retweet) %>%
distinct(retweet_text,.keep_all = TRUE )
retweets_sentiment <- retweets %>%
bind_cols(get_nrc_sentiment(as.vector(
retweets$retweet_text_cleaned),
language = 'dutch'))
retweets_sentiment_small <- retweets_sentiment %>%
select(screen_name,by_day,by_day_retweet,retweet_text,retweet_screen_name, anger:positive)
# retweets_sentiment%>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/retweets_sentiment_v_{date}','.csv') )
### Sentiment of Quotes
#### ----------------------------------------------------
quotes <- filtered_tweets %>%
filter(is_quote) %>%
distinct(quoted_text,.keep_all = TRUE)
quotes_sentiment <- quotes %>%
bind_cols(get_nrc_sentiment(as.vector(
quotes$quoted_text_cleaned),
language = 'dutch'))
quotes_sentiment_small <- quotes_sentiment %>%
select(screen_name,by_day,by_day_quoted,quoted_text,quoted_screen_name,anger:positive)
# quotes_sentiment %>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/quotes_sentiment_v_{date}','.csv') )
# compute statistics
#------------------------------------------------------------------------------------------------
distinct_filtered_tweets_sentiment_text_count <- distinct_filtered_tweets_sentiment_text %>%
unnest_tokens(input = text_cleaned,output = word) %>%
inner_join(get_sentiment_dictionary('nrc',language ='dutch')) %>%
count(sentiment)
distinct_filtered_tweets_sentiment_words <- distinct_filtered_tweets_sentiment_text %>%
unnest_tokens(input = text_cleaned,output = word) %>%
inner_join(get_sentiment_dictionary('nrc',language ='dutch'))
### Bots
#---------------------------------------------------------------------------------------------------
# distinct_tweets_sentiment_words %>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/distinct_tweets_sentiment_words_v_{date}','.csv') )
filtered_tweets_sentiment_tweet_text <- read_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_sentiment_tweet_text_v_{date}','.csv') )
bots_info <- read_csv('../vaccinationinNL/tmp/bot/bots_df_info_news2020-07-25.csv')
bots_info <- bots_info %>%
rename(screen_name_bot = screen_name)
bot_distinct_filtered_tweets_sentiment_text <- distinct_filtered_tweets_sentiment_text %>%
left_join(bots_info,by =c( 'screen_name'='screen_name_bot'))
bot_retweets_sentiment <- retweets_sentiment %>%
left_join(bots_info,by =c( 'retweet_screen_name'='screen_name_bot'))
bot_quotes_sentiment <- quotes_sentiment %>%
left_join(bots_info,by =c( 'quoted_screen_name'='screen_name_bot'))
bot_filltered_tweets_sentiment <-filtered_tweets_sentiment_tweet_text %>%
left_join(bots_info,by =c( 'screen_name'='screen_name_bot'))
#merge sentiments_bots
#-------------------------------------------------------
bot_tweets_retweets_quotes_sentiment_merged <- bind_rows(bot_distinct_filtered_tweets_sentiment_text,bot_retweets_sentiment,bot_quotes_sentiment)
# sentiment_merged%>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/sentiment_merged_v_{date}','.csv') )
## Hashtags
##------------------------------------------------------------------
twitter_hashtags_bot_cuont <- bot_distinct_filtered_tweets_sentiment_text %>%
select(hashtags,prob_bot) %>%
filter(!is.na(hashtags)) %>%
separate_rows(hashtags, sep = ' ')
### topic modeling
##-----------------------------------------------------------
d_sparse <- distinct_filtered_tweets_sentiment_text %>%
mutate(n_words = map_dbl(text_cleaned, str_count),
text_cleaned = rm_twitter_url(text_cleaned),
text_cleaned = rm_white_punctuation(text_cleaned),
text_cleaned = str_remove_all(text_cleaned,'t.co')) %>%
filter(n_words > 10) %>%
rowid_to_column() %>%
unnest_tokens(input = text_cleaned ,output = word,drop = FALSE) %>%
anti_join(dutch_stop_words) %>%
anti_join(tibble(word = stopwords()) ) %>%
# add_count(word) %>%
#select(word,n) %>%
#arrange(n)
#filter(n > 10) %>%
#select(-n) %>%
count(rowid,word,sort = TRUE) %>%
#filter(n > 30) %>%
cast_sparse(rowid,word,n)
m_topic_tweets <- stm(d_sparse, K = 16, verbose = TRUE, max.em.its = 75)
d_beta_tweet <- tidy(m_topic_tweets)
# select only one of the terms that map to the same lemmatised term
d_top_terms_tweet <- d_beta_tweet %>%
group_by(topic, term) %>%
summarise(beta = max(beta)) %>%
# select the top words by topic
group_by(topic) %>%
top_n(100, beta) %>%
mutate(top_word = beta == max(beta)) %>%
arrange(topic) %>%
ungroup() %>%
rename(term = term)
usethis::use_data(distinct_filtered_tweets_sentiment_text, overwrite = TRUE)
usethis::use_data(retweets_sentiment_small, overwrite = TRUE)
usethis::use_data(quotes_sentiment_small, overwrite = TRUE)
usethis::use_data(distinct_filtered_tweets_sentiment_text_count, overwrite = TRUE)
usethis::use_data(distinct_filtered_tweets_sentiment_words, overwrite = TRUE)
usethis::use_data(filtered_tweets, overwrite = TRUE)
usethis::use_data(bot_tweets_retweets_quotes_sentiment_merged, overwrite = TRUE)
#usethis::use_data(twitter_hashtags_cuont, overwrite = TRUE)
#usethis::use_data(bot_filltered_tweets, overwrite = TRUE)
usethis::use_data(bot_filltered_tweets_sentiment, overwrite = TRUE)
usethis::use_data(d_top_terms_tweet, overwrite = TRUE)
#usethis::use_data(twitter_hashtags_bot_cuont, overwrite = TRUE)
mcnakhaee/VaccinationinNLShiny documentation built on Aug. 4, 2020, 1:07 a.m.
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## code to prepare `collect_news` dataset goes here
##-----------------------------------------------------------------------
library(tidyverse)
library(rtweet)
library(glue)
library(qdapRegex)
library(tidytext)
library(syuzhet)
library(lubridate)
date <- Sys.Date()
#Keywords Collect Tweets
##-----------------------------------------------------------------------
tweet_keywords <- c('vaccineren',
'vaccineren autism',
'vaccineren mmr',
'vaccineren measles',
'vaccineren corona',
'vaccineren coronavirus',
'vaccineren covid',
'vaccinatie autism',
'vaccinatie corona',
'Vaccinatie covid',
'Vaccinatie autism',
'vaxxhappened',
'CoronaVaccin',
'plandemic',
'autism vaccine',
'vrijheid vaccine',
'mmr vaccine',
'measles autism',
'Vaccinatie',
'antivax',
'vaccination autism',
'vaccination corona',
'antivax corona',
'antivax covid',
'coronavaccin',
'viruswaanzin',
'ikvaccineer',
'ikvaccineer',
'ikvaccineerniet',
'Gezondheidsraad',
'MyBodyMyChoice'
)
tweets_in_dutch <- data.frame()
tweets_in_news <- data.frame()
tweets_in_verified <- data.frame()
for (keyword in tweet_keywords) {
Sys.sleep(20)
tweet_tmp <- rtweet::search_tweets(keyword,n = 3000,retryonratelimit = TRUE,lang = "nl")
tweet_tmp$searched_keyword <- keyword
tweets_in_dutch <- bind_rows(tweets_in_dutch,tweet_tmp)
}
tweets_in_dutch %>%
save_as_csv(glue('../vaccinationinNL/tmp/tweets_in_dutch{date}','.csv'), prepend_ids = TRUE, na = "", fileEncoding = "UTF-8")
# preprocess tweets
##-------------------------------------------------------------------------------------------------------------------------------------
tweet_files <- list.files('../vaccinationinNL/tmp') %>%
.[str_detect(.,'tweets_in_dutch')]
tweet_files <- paste('../vaccinationinNL/tmp/',tweet_files,sep = '')
tweet_dfs <-purrr::map(tweet_files,rtweet::read_twitter_csv)
tweets_all <- data.frame()
for (df in tweet_dfs) {
tweets_all <- bind_rows(tweets_all,df)
}
## process anc clean text
filtered_tweets <- tweets_all %>%
distinct_all %>%
filter(str_detect(text,'vaccin') | str_detect(text,'Vaccin')| str_detect(text,'vax') | str_detect(text,'Vax')) %>%
mutate(text_cleaned = str_replace_all(text,"@\\w+", ""),
text_cleaned = str_replace_all(text_cleaned,"RT @[a-z,A-Z]*: ", ""),
text_cleaned = str_replace_all(text_cleaned,"http\\w+", ""),
text_cleaned = str_replace_all(text_cleaned,"#[a-z,A-Z]*", ""),
text_cleaned = rm_url(text_cleaned),
retweet_text_cleaned = str_replace_all(retweet_text,"@\\w+", ""),
retweet_text_cleaned = str_replace_all(retweet_text_cleaned,"RT @[a-z,A-Z]*: ", ""),
retweet_text_cleaned = str_replace_all(retweet_text_cleaned,"http\\w+", ""),
retweet_text_cleaned = str_replace_all(retweet_text_cleaned,"#[a-z,A-Z]*", ""),
retweet_text_cleaned = rm_url(retweet_text_cleaned),
quoted_text_cleaned = str_replace_all(quoted_text,"@\\w+", ""),
quoted_text_cleaned = str_replace_all(quoted_text_cleaned,"RT @[a-z,A-Z]*: ", ""),
quoted_text_cleaned = str_replace_all(quoted_text_cleaned,"http\\w+", ""),
quoted_text_cleaned = str_replace_all(quoted_text_cleaned,"#[a-z,A-Z]*", ""),
quoted_text_cleaned = rm_url(quoted_text_cleaned),
created_at = lubridate::ymd_hms(created_at),
quoted_created_at = lubridate::ymd_hms(quoted_created_at),
retweet_created_at = lubridate::ymd_hms(quoted_created_at),
by_day = lubridate::floor_date(created_at, "day"),
by_hour= lubridate::floor_date(created_at, "hour"),
by_day_quoted= lubridate::floor_date(quoted_created_at, "day"),
by_hour_quoted= lubridate::floor_date(quoted_created_at, "hour"),
by_day_retweet = lubridate::floor_date(retweet_created_at, "day"),
by_hour_retweet= lubridate::floor_date(retweet_created_at, "hour")
)
filtered_tweets%>%
select(screen_name,
text,
source,
mentions_screen_name,
retweet_screen_name,
quoted_screen_name,
created_at,
by_day,
by_hour,
by_day_retweet,
by_hour_retweet,
by_day_quoted,
by_hour_quoted,
hashtags,
is_retweet,
is_quote,
followers_count,
favorite_count,
retweet_count,
quote_count,
reply_count,
symbols,
location,
urls_url,
favourites_count,
friends_count,
retweet_text,
quoted_text,
verified,
text_cleaned,
retweet_text_cleaned,
quoted_text_cleaned) %>%
write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_v_{date}','.csv') )
#Sentiment Analysis
#---------------------------------------------------------------------------------
filtered_tweets <- read_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_v_{date}','.csv') )
###-------------------------------------------
filtered_tweets_sentiment_tweet_text <- filtered_tweets %>%
bind_cols(get_nrc_sentiment(as.vector(
filtered_tweets$text_cleaned),
language = 'dutch'))
filtered_tweets_sentiment_tweet_text %>%
write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_sentiment_tweet_text_v_{date}','.csv') )
### ------------------------------------
distinct_filtered_tweets <- filtered_tweets %>%
distinct(text,.keep_all = TRUE)
distinct_filtered_tweets_sentiment_text <- distinct_filtered_tweets %>%
bind_cols(get_nrc_sentiment(as.vector(
distinct_filtered_tweets$text_cleaned),
language = 'dutch'))
# distinct_tweets_sentiment_text%>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/distinct_tweets_sentiment_text_v_{date}','.csv') )
#------------------------
### Sentiment of retweets
#### --------------------
retweets <- filtered_tweets %>%
filter(is_retweet) %>%
distinct(retweet_text,.keep_all = TRUE )
retweets_sentiment <- retweets %>%
bind_cols(get_nrc_sentiment(as.vector(
retweets$retweet_text_cleaned),
language = 'dutch'))
retweets_sentiment_small <- retweets_sentiment %>%
select(screen_name,by_day,by_day_retweet,retweet_text,retweet_screen_name, anger:positive)
# retweets_sentiment%>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/retweets_sentiment_v_{date}','.csv') )
### Sentiment of Quotes
#### ----------------------------------------------------
quotes <- filtered_tweets %>%
filter(is_quote) %>%
distinct(quoted_text,.keep_all = TRUE)
quotes_sentiment <- quotes %>%
bind_cols(get_nrc_sentiment(as.vector(
quotes$quoted_text_cleaned),
language = 'dutch'))
quotes_sentiment_small <- quotes_sentiment %>%
select(screen_name,by_day,by_day_quoted,quoted_text,quoted_screen_name,anger:positive)
# quotes_sentiment %>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/quotes_sentiment_v_{date}','.csv') )
# compute statistics
#------------------------------------------------------------------------------------------------
distinct_filtered_tweets_sentiment_text_count <- distinct_filtered_tweets_sentiment_text %>%
unnest_tokens(input = text_cleaned,output = word) %>%
inner_join(get_sentiment_dictionary('nrc',language ='dutch')) %>%
count(sentiment)
distinct_filtered_tweets_sentiment_words <- distinct_filtered_tweets_sentiment_text %>%
unnest_tokens(input = text_cleaned,output = word) %>%
inner_join(get_sentiment_dictionary('nrc',language ='dutch'))
### Bots
#---------------------------------------------------------------------------------------------------
# distinct_tweets_sentiment_words %>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/distinct_tweets_sentiment_words_v_{date}','.csv') )
filtered_tweets_sentiment_tweet_text <- read_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/filtered_tweets_sentiment_tweet_text_v_{date}','.csv') )
bots_info <- read_csv('../vaccinationinNL/tmp/bot/bots_df_info_news2020-07-25.csv')
bots_info <- bots_info %>%
rename(screen_name_bot = screen_name)
bot_distinct_filtered_tweets_sentiment_text <- distinct_filtered_tweets_sentiment_text %>%
left_join(bots_info,by =c( 'screen_name'='screen_name_bot'))
bot_retweets_sentiment <- retweets_sentiment %>%
left_join(bots_info,by =c( 'retweet_screen_name'='screen_name_bot'))
bot_quotes_sentiment <- quotes_sentiment %>%
left_join(bots_info,by =c( 'quoted_screen_name'='screen_name_bot'))
bot_filltered_tweets_sentiment <-filtered_tweets_sentiment_tweet_text %>%
left_join(bots_info,by =c( 'screen_name'='screen_name_bot'))
#merge sentiments_bots
#-------------------------------------------------------
bot_tweets_retweets_quotes_sentiment_merged <- bind_rows(bot_distinct_filtered_tweets_sentiment_text,bot_retweets_sentiment,bot_quotes_sentiment)
# sentiment_merged%>%
# write_csv(glue('../vaccinationinNL/tmp/tweets_preprocessed/sentiment_merged_v_{date}','.csv') )
## Hashtags
##------------------------------------------------------------------
twitter_hashtags_bot_cuont <- bot_distinct_filtered_tweets_sentiment_text %>%
select(hashtags,prob_bot) %>%
filter(!is.na(hashtags)) %>%
separate_rows(hashtags, sep = ' ')
### topic modeling
##-----------------------------------------------------------
d_sparse <- distinct_filtered_tweets_sentiment_text %>%
mutate(n_words = map_dbl(text_cleaned, str_count),
text_cleaned = rm_twitter_url(text_cleaned),
text_cleaned = rm_white_punctuation(text_cleaned),
text_cleaned = str_remove_all(text_cleaned,'t.co')) %>%
filter(n_words > 10) %>%
rowid_to_column() %>%
unnest_tokens(input = text_cleaned ,output = word,drop = FALSE) %>%
anti_join(dutch_stop_words) %>%
anti_join(tibble(word = stopwords()) ) %>%
# add_count(word) %>%
#select(word,n) %>%
#arrange(n)
#filter(n > 10) %>%
#select(-n) %>%
count(rowid,word,sort = TRUE) %>%
#filter(n > 30) %>%
cast_sparse(rowid,word,n)
m_topic_tweets <- stm(d_sparse, K = 16, verbose = TRUE, max.em.its = 75)
d_beta_tweet <- tidy(m_topic_tweets)
# select only one of the terms that map to the same lemmatised term
d_top_terms_tweet <- d_beta_tweet %>%
group_by(topic, term) %>%
summarise(beta = max(beta)) %>%
# select the top words by topic
group_by(topic) %>%
top_n(100, beta) %>%
mutate(top_word = beta == max(beta)) %>%
arrange(topic) %>%
ungroup() %>%
rename(term = term)
usethis::use_data(distinct_filtered_tweets_sentiment_text, overwrite = TRUE)
usethis::use_data(retweets_sentiment_small, overwrite = TRUE)
usethis::use_data(quotes_sentiment_small, overwrite = TRUE)
usethis::use_data(distinct_filtered_tweets_sentiment_text_count, overwrite = TRUE)
usethis::use_data(distinct_filtered_tweets_sentiment_words, overwrite = TRUE)
usethis::use_data(filtered_tweets, overwrite = TRUE)
usethis::use_data(bot_tweets_retweets_quotes_sentiment_merged, overwrite = TRUE)
#usethis::use_data(twitter_hashtags_cuont, overwrite = TRUE)
#usethis::use_data(bot_filltered_tweets, overwrite = TRUE)
usethis::use_data(bot_filltered_tweets_sentiment, overwrite = TRUE)
usethis::use_data(d_top_terms_tweet, overwrite = TRUE)
#usethis::use_data(twitter_hashtags_bot_cuont, overwrite = TRUE)
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Add the following code to your website.
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