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jo_06_fb_sentiment.R
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load("data.Rdata")
library(dplyr)
library(tidytext)
# ------------------ tidytext: sentiment analysis --------------
tidy_fb_comments <- fb_comments %>%
tidytext::unnest_tokens(word, com_text) %>%
anti_join(stop_words, by = "word")
# -------------- tidytext: wordcount and n-grams ---------------
library(tidytext)
comments_words_fb <- tidy_fb_comments %>%
count(word, sort = TRUE) %>%
top_n(50)
comments_2grams_fb <- fb_comments %>%
tidytext::unnest_tokens(two_gram, com_text, token = "ngrams", n = 2) %>%
count(two_gram, sort = TRUE) %>%
top_n(20)
comments_3grams_fb <- fb_comments %>%
tidytext::unnest_tokens(three_gram, com_text, token = "ngrams", n = 3) %>%
count(three_gram, sort = TRUE) %>%
top_n(20)
comments_4grams_fb <- fb_comments %>%
tidytext::unnest_tokens(four_gram, com_text, token = "ngrams", n = 4) %>%
count(four_gram, sort = TRUE) %>%
top_n(20)
comments_5grams_fb <- fb_comments %>%
tidytext::unnest_tokens(five_gram, com_text, token = "ngrams", n = 5) %>%
count(five_gram, sort = TRUE) %>%
top_n(20)
comments_6grams_fb <- fb_comments %>%
tidytext::unnest_tokens(six_gram, com_text, token = "ngrams", n = 6) %>%
count(six_gram, sort = TRUE) %>%
top_n(20)
# ---------- sentiment by comment --------
library(tidyr)
fb_comment_sent_bing <- tidy_fb_comments %>%
inner_join(get_sentiments("bing"), by = "word") %>%
count(com_id, sentiment) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment_bing = positive - negative) %>%
ungroup() %>%
left_join(fb_comments, by = "com_id")
fb_comment_sent_afinn <- tidy_fb_comments %>%
count(com_id, word) %>%
ungroup() %>%
inner_join(get_sentiments("afinn"), by = "word") %>%
group_by(com_id) %>%
summarize(score_afinn = sum(score * n / sum(n))) %>%
left_join(fb_comments, by = "com_id")
# sentiment youtube total
fb_comment_sent_bing %>%
summarise(positive = sum(positive),
negative = sum(negative),
sent_mean = mean(sentiment_bing),
sentiment = positive - negative)
fb_comment_sent_afinn %>%
summarize(score_afinn_mean = mean(score_afinn),
score_afinn = sum(score_afinn))
# sentiment youtube total - BY MONTH
fb_month_sent_bing <- fb_comment_sent_bing %>%
group_by(month = format(com_created, "%Y-%m")) %>%
filter(n() >= 100) %>%
summarise(pos_bing = sum(positive),
neg_bing = sum(negative),
sent_mean = mean(sentiment_bing),
sent_pos_menos_neg = pos_bing - neg_bing)
fb_month_sent_afinn <- fb_comment_sent_afinn %>%
group_by(month = format(com_created, "%Y-%m")) %>%
filter(n() >= 100) %>%
summarise(score_afinn = mean(score_afinn))
fb_month_sent <- full_join(fb_month_sent_bing, fb_month_sent_afinn, by = "month")
# rm(fb_month_sent_bing, fb_month_sent_afinn)
library(ggplot2)
fb_month_sent %>%
ggplot(aes(x = month)) +
geom_line(aes(y = sent_mean, group = 1), color = "red") +
geom_line(aes(y = score_afinn, group = 1), color = "green") +
geom_hline(yintercept = 0) +
theme_bw() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(),
axis.line = element_line(colour = "grey"), legend.position = "none",
panel.grid.major = element_blank(), panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1))
# sentiment facebook by title
fb_comment_sent_bing2 <- fb_comment_sent_bing %>% left_join(yt_comments, by = "video_id")
fb_comment_sent_afinn2 <- fb_comment_sent_afinn %>% left_join(yt_comments, by = "video_id")
fb_title_sent_bing <- fb_comment_sent_bing2 %>%
group_by(short_title.x, vid_created) %>%
summarise(pos_bing = sum(positive),
neg_bing = sum(negative),
sent_mean = mean(sentiment_bing),
sent_pos_menos_neg = pos_bing - neg_bing) %>%
ungroup() %>%
arrange(-sent_pos_menos_neg)
fb_title_sent_afinn <- fb_comment_sent_afinn2 %>%
group_by(short_title.x, vid_created) %>%
filter(n() >= 100) %>%
summarise(score_afinn = mean(score_afinn))
fb_title_sent <- full_join(fb_title_sent_bing, fb_title_sent_afinn, by = "short_title.x")
# rm(fb_title_sent_bing, fb_title_sent_afinn)
library(ggplot2)
fb_title_sent %>%
ggplot(aes(x = reorder(short_title.x, vid_created.x))) +
geom_line(aes(y = sent_mean, group = 1), color = "red") +
geom_line(aes(y = score_afinn, group = 1), color = "green") +
geom_hline(yintercept = 0) +
theme_bw() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(),
axis.line = element_line(colour = "grey"), legend.position = "none",
panel.grid.major = element_blank(), panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1))
# --------- most common words ------
most_used <- tidy_fb_comments %>%
inner_join(get_sentiments("bing"), by = "word") %>%
count(word, sentiment, sort = TRUE) %>%
ungroup() %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(y = "Contribution to sentiment",
x = NULL) +
coord_flip()
# I see trump as a positive word! and funny as a negative one...
custom_stop_words <- bind_rows(data_frame(word = c("trump"),
lexicon = c("custom")),
data_frame(word = c("funny"),
lexicon = c("custom")),
stop_words)
tidy_fb_comments_2 <- fb_comments %>%
tidytext::unnest_tokens(word, com_text) %>%
anti_join(custom_stop_words, by = "word")
most_used_2 <- tidy_fb_comments_2 %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()
most_used_2 %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(y = "Contribution to sentiment",
x = NULL) +
coord_flip()
# --------- sentiments with nrc -------
tidy_fb_comments %>%
inner_join(get_sentiments("nrc"), by = "word") %>%
count(word, sentiment, sort = TRUE) %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(y = "Contribution to sentiment",
x = NULL) +
coord_flip()
# john is bad and trump is surprise!!
custom_stop_words <- bind_rows(data_frame(word = c("john"),
lexicon = c("custom")),
custom_stop_words)
tidy_fb_comments_3 <- fb_comments %>%
tidytext::unnest_tokens(word, com_text) %>%
anti_join(custom_stop_words, by = "word")
tidy_fb_comments_3 %>%
inner_join(get_sentiments("nrc"), by = "word") %>%
count(word, sentiment, sort = TRUE) %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(y = "Contribution to sentiment",
x = NULL) +
coord_flip()
# comment on money!
tidy_fb_comments_3 %>%
inner_join(get_sentiments("nrc"), by = "word") %>%
count(word, sentiment, sort = TRUE) %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
mutate(sentiment = reorder(sentiment, n),
pos_neg = ifelse(sentiment %in% c("positive", "anticipation", "joy", "trust", "surprise"), "Positive", "Negative")) %>%
ggplot(aes(sentiment, n)) +
geom_col(aes(fill = pos_neg), show.legend = FALSE) +
# facet_wrap(~sentiment, scales = "free_y") +
labs(y = "Contribution to sentiment",
x = NULL) +
coord_flip()
# ---------- sentiments extreme --------------
library(ggplot2)
extremes_sentiment_fb <- head(fb_title_sent_bing, 10) %>%
bind_rows(tail(fb_title_sent_bing, 10)) %>%
mutate(sentiment = sent_pos_menos_neg,
short_title = reorder(short_title.x, -sentiment)) %>%
ggplot(aes(reorder(short_title, -sentiment), sentiment, fill = sentiment > 0)) +
geom_col() +
coord_flip() +
theme_bw() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(),
axis.line = element_line(colour = "grey"), legend.position = "none",
panel.grid.major = element_blank(), panel.border = element_blank())
save.image("data.Rdata")