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library(rtweet)
## store api keys (these are fake example values; replace with your own keys) api_key <- "NJrF9CgvH7GAijq5R53qyo2su"
api_secret_key <- "7xfRNSFOkDIiRUToPcbW7n1pSxSSKeNSueXRJombQBrTdx6ePB"
## authenticate via web browser token <- create_token(
app = "test_bd_upnvj", consumer_key = api_key,
consumer_secret = api_secret_key)
#Cari tweet tentang topik pilihan Anda,
#persempit jumlah tweet yang diinginkan dan putuskan untuk memasukkan retweet atau tidak.
kata <- search_tweets("#VACCINE", n=1000, include_rts = FALSE) kata
#Proses setiap set tweet menjadi teks rapi atau objek corpus.
tweet.Kata = kata %>% select(screen_name, text) tweet.Kata
head(tweet.Kata$text)
#menghapus element http
tweet.Kata$stripped_text1 <- gsub("http\\S+","",tweet.Kata$text)
#gunakan fungsi unnest_tokens() untuk konversi menjadi huruf kecil
#hapus tanda baca, dan id untuk setiap tweet tweet.Kata_stem <- tweet.Kata %>%
select(stripped_text1) %>%
unnest_tokens(word, stripped_text1) head(tweet.Kata_stem)
#hapus kata-kata stopwords dari daftar kata-kata cleaned_tweets.Kata <- tweet.Kata_stem %>%
anti_join(stop_words) head(cleaned_tweets.Kata)
head(tweet.Kata$text)
#1000 kata teratas di tweet #VACCINE cleaned_tweets.Kata %>%
count(word, sort = TRUE) %>%
top_n(1000) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(x=word, y = n)) +
#Untuk melakukan analisis sentimen menggunakan Bing di tweet VACCINE,
#perintah berikut ini mengembalikan sebuah tibble.
bing_kata = cleaned_tweets.Kata %>% inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>% ungroup()
#visualisasi jumlah kata,
#Anda dapat memfilter dan memplot kata-kata bersebelahan untuk membandingkan
#emosi positif dan negatif.
bing_kata %>% 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(title = "Tweets containing '#COVID19'", y = "Contribution to sentiment", x = NULL) + coord_flip() + theme_bw()
#Fungsi untuk mendapatkan skor sentimen untuk setiap tweet sentiment_bing = function(twt){
twt_tbl = tibble(text = twt) %>%
mutate(
stripped_text = gsub("http\\S+","",text) )%>%
unnest_tokens(word, stripped_text) %>%
anti_join(stop_words) %>%
inner_join(get_sentiments("bing")) %>%
count(word,sentiment, sort = TRUE) %>%
ungroup() %>%
#buat kolom "skor", yang menetapkan -1 untuk semua kata negatif, #dan 1 untuk kata positif
#menghitung total score sent.score = case_when(
nrow(twt_tbl)==0~0, #jika tidak ada kata, skor adalah 0
nrow(twt_tbl)>0~sum(twt_tbl$score) #selainnya, jumlah positif dan negatif )
#untuk melacak tweet mana yang tidak mengandung kata sama sekali dari daftar bing zero.type = case_when(
nrow(twt_tbl)==0~"Type 1", #Type 1: tidak ada kata sama sekali, zero = no nrow(twt_tbl)>0~"Type 2" #Type 2: nol berarti jumlah kata = 0
)
list(score = sent.score, type = zero.type, twt_tbl = twt_tbl) }
#menerapkan fungsi
#Fungsi lapply mengembalikan list semua skor sentimen, jenis, dan tabel tweet kata_sent = lapply(kata$text, function(x){sentiment_bing(x)})
kata_sent
#membuat tibble yang menentukan kata, skor, dan jenisnya kata_sentiment = bind_rows(tibble(kata = '#VACCINE', score = unlist(map(kata_sent, 'score')), type = unlist(map(kata_sent, 'type'))))
#kita dapat melihat beberapa karakteristik sentimen di setiap kelompok.
#Berikut adalah histogram sentimen tweet.
ggplot(kata_sentiment, aes(x=score, fill = kata)) +
geom_histogram(bins = 15, alpha= .6) + facet_grid(~kata) + theme_bw()