R語言進行Twitter資料視覺化
作者|Audhi Aprilliant
編譯|VK
來源|Towards Datas Science
概述
對於這個專案,我們在2019年5月28-29日通過爬蟲來使用Twitter的原始資料。此外,資料是CSV格式(逗號分隔),可以在這裡下載。
它涉及兩個主題,一個是包含關鍵字“Joko Widodo”的Joko Widodo的資料,另一個是帶有關鍵字“Prabowo Subianto”的Prabowo Subianto的資料。其中包括幾個變數和資訊,以確定使用者情緒。實際上,資料有16個變數或屬性和1000多個觀察值。表1列出了一些變數。
# 匯入庫 library(ggplot2) library(lubridate) # 載入Joko Widodo的資料 data.jokowi.df = read.csv(file = 'data-joko-widodo.csv', header = TRUE, sep = ',') senti.jokowi = read.csv(file = 'sentiment-joko-widodo.csv', header = TRUE, sep = ',') # 載入Prabowo Subianto的資料 data.prabowo.df = read.csv(file = 'data-prabowo-subianto.csv', header = TRUE, sep = ',') senti.prabowo = read.csv(file = 'sentiment-prabowo-subianto.csv', header = TRUE, sep = ',')
資料視覺化
資料探索旨在從Twitter資料中獲取任何資訊。應該指出的是,資料已經進行了文字預處理。我們對那些被認為是很有趣的變數進行探索。。
# TWEETS的條形圖-JOKO WIDODO data.jokowi.df$created = ymd_hms(data.jokowi.df$created, tz = 'Asia/Jakarta') # 另一種製作“date”和“hour”變數的方法 data.jokowi.df$date = date(data.jokowi.df$created) data.jokowi.df$hour = hour(data.jokowi.df$created) # 日期2019-05-29 data.jokowi.date1 = subset(x = data.jokowi.df, date == '2019-05-29') data.hour.date1 = data.frame(table(data.jokowi.date1$hour)) colnames(data.hour.date1) = c('Hour','Total.Tweets') # 建立資料視覺化 ggplot(data.hour.date1)+ geom_bar(aes(x = Hour, y = Total.Tweets, fill = I('blue')), stat = 'identity', alpha = 0.75, show.legend = FALSE)+ geom_hline(yintercept = mean(data.hour.date1$Total.Tweets), col = I('black'), size = 1)+ geom_text(aes(fontface = 'italic', label = paste('Average:', ceiling(mean(data.hour.date1$Total.Tweets)), 'Tweets per hour'), x = 8, y = mean(data.hour.date1$Total.Tweets)+20), hjust = 'left', size = 4)+ labs(title = 'Total Tweets per Hours - Joko Widodo', subtitle = '28 May 2019', caption = 'Twitter Crawling 28 - 29 May 2019')+ xlab('Time of Day')+ ylab('Total Tweets')+ scale_fill_brewer(palette = 'Dark2')+ theme_bw() # TWEETS的條形圖-PRABOWO SUBIANTO data.prabowo.df$created = ymd_hms(data.prabowo.df$created, tz = 'Asia/Jakarta') # 另一種製作“date”和“hour”變數的方法 data.prabowo.df$date = date(data.prabowo.df$created) data.prabowo.df$hour = hour(data.prabowo.df$created) # 日期2019-05-28 data.prabowo.date1 = subset(x = data.prabowo.df, date == '2019-05-28') data.hour.date1 = data.frame(table(data.prabowo.date1$hour)) colnames(data.hour.date1) = c('Hour','Total.Tweets') # 日期 2019-05-29 data.prabowo.date2 = subset(x = data.prabowo.df, date == '2019-05-29') data.hour.date2 = data.frame(table(data.prabowo.date2$hour)) colnames(data.hour.date2) = c('Hour','Total.Tweets') data.hour.date3 = rbind(data.hour.date1,data.hour.date2) data.hour.date3$Date = c(rep(x = '2019-05-28', len = nrow(data.hour.date1)), rep(x = '2019-05-29', len = nrow(data.hour.date2))) data.hour.date3$Labels = c(letters,'A','B') data.hour.date3$Hour = as.character(data.hour.date3$Hour) data.hour.date3$Hour = as.numeric(data.hour.date3$Hour) # 資料預處理 for (i in 1:nrow(data.hour.date3)) { if (i%%2 == 0) { data.hour.date3[i,'Hour'] = '' } if (i%%2 == 1) { data.hour.date3[i,'Hour'] = data.hour.date3[i,'Hour'] } } data.hour.date3$Hour = as.factor(data.hour.date3$Hour) # 資料視覺化 ggplot(data.hour.date3)+ geom_bar(aes(x = Labels, y = Total.Tweets, fill = Date), stat = 'identity', alpha = 0.75, show.legend = TRUE)+ geom_hline(yintercept = mean(data.hour.date3$Total.Tweets), col = I('black'), size = 1)+ geom_text(aes(fontface = 'italic', label = paste('Average:', ceiling(mean(data.hour.date3$Total.Tweets)), 'Tweets per hour'), x = 5, y = mean(data.hour.date3$Total.Tweets)+6), hjust = 'left', size = 3.8)+ scale_x_discrete(limits = data.hour.date3$Labels, labels = data.hour.date3$Hour)+ labs(title = 'Total Tweets per Hours - Prabowo Subianto', subtitle = '28 - 29 May 2019', caption = 'Twitter Crawling 28 - 29 May 2019')+ xlab('Time of Day')+ ylab('Total Tweets')+ ylim(c(0,100))+ theme_bw()+ theme(legend.position = 'bottom', legend.title = element_blank())+ scale_fill_brewer(palette = 'Dark2')
根據圖1,我們可以得出結論,通過資料抓取(關鍵字“Jokow Widodo”和“Prabowo Subianto”)得到的tweet數量並不相似,即使在同一日期。
例如,在圖1(左)中,從視覺上看,對於關鍵字為“Joko Widodo”的推文,僅在2019年5月28日03:00–17:00 WIB期間獲得。而在圖1(右圖)中,我們得出的結論是,在2019年5月28日至29日12:00-23:59 WIB(2019年5月28日)和00:00-15:00 WIB(2019年5月29日)期間獲得的關鍵詞為“Prabowo Subianto”的推文。
# 2019-05-28的推特
ggplot(data.hour.date1)+
geom_bar(aes(x = Hour,
y = Total.Tweets,
fill = I('red')),
stat = 'identity',
alpha = 0.75,
show.legend = FALSE)+
geom_hline(yintercept = mean(data.hour.date1$Total.Tweets),
col = I('black'),
size = 1)+
geom_text(aes(fontface = 'italic',
label = paste('Average:',
ceiling(mean(data.hour.date1$Total.Tweets)),
'Tweets per hour'),
x = 6.5,
y = mean(data.hour.date1$Total.Tweets)+5),
hjust = 'left',
size = 4)+
labs(title = 'Total Tweets per Hours - Prabowo Subianto',
subtitle = '28 May 2019',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlab('Time of Day')+
ylab('Total Tweets')+
ylim(c(0,100))+
theme_bw()+
scale_fill_brewer(palette = 'Dark2')
# 2019-05-29的推特
ggplot(data.hour.date2)+
geom_bar(aes(x = Hour,
y = Total.Tweets,
fill = I('red')),
stat = 'identity',
alpha = 0.75,
show.legend = FALSE)+
geom_hline(yintercept = mean(data.hour.date2$Total.Tweets),
col = I('black'),
size = 1)+
geom_text(aes(fontface = 'italic',
label = paste('Average:',
ceiling(mean(data.hour.date2$Total.Tweets)),
'Tweets per hour'),
x = 1,
y = mean(data.hour.date2$Total.Tweets)+6),
hjust = 'left',
size = 4)+
labs(title = 'Total Tweets per Hours - Prabowo Subianto',
subtitle = '29 May 2019',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlab('Time of Day')+
ylab('Total Tweets')+
ylim(c(0,100))+
theme_bw()+
scale_fill_brewer(palette = 'Dark2')
根據圖2,我們得到了使用關鍵字“Joko Widodo”和“Prabowo Subianto”的使用者之間的顯著差異。關鍵詞為“Joko Widodo”的tweet在某個特定時間(07:00–09:00 WIB)談論Joko Widodo往往非常激烈,08:00 WIB的tweet數量最多。它有348條推文。然而,在2019年5月28日至29日期間,關鍵詞為“Prabowo Subianto”的推文往往會不斷地談論Prabowo Subianto。2019年5月28日至29日,每小時上傳關鍵詞為“Prabowo Subianto”的推文平均為36條。
# JOKO WIDODO
df.score.1 = subset(senti.jokowi,class == c('Negative','Positive'))
colnames(df.score.1) = c('Score','Text','Sentiment')
# Data viz
ggplot(df.score.1)+
geom_density(aes(x = Score,
fill = Sentiment),
alpha = 0.75)+
xlim(c(-11,11))+
labs(title = 'Density Plot of Sentiment Scores',
subtitle = 'Joko Widodo',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlab('Score')+
ylab('Density')+
theme_bw()+
scale_fill_brewer(palette = 'Dark2')+
theme(legend.position = 'bottom',
legend.title = element_blank())
# PRABOWO SUBIANTO
df.score.2 = subset(senti.prabowo,class == c('Negative','Positive'))
colnames(df.score.2) = c('Score','Text','Sentiment')
ggplot(df.score.2)+
geom_density(aes(x = Score,
fill = Sentiment),
alpha = 0.75)+
xlim(c(-11,11))+
labs(title = 'Density Plot of Sentiment Scores',
subtitle = 'Prabowo Subianto',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlab('Density')+
ylab('Score')+
theme_bw()+
scale_fill_brewer(palette = 'Dark2')+
theme(legend.position = 'bottom',
legend.title = element_blank())
圖3是2019年5月28日至29日以“Joko Widodo”和“Prabowo Subianto”為關鍵詞的多條推文的條形圖。由圖3(左)可以得出,Twitter使用者在19:00-23:59 WIB上談論Prabowo Subianto的頻率較低。這是由於印尼人的休息時間造成的。然而,這些帶有主題的推文總是在午夜更新,因為有的使用者居住在國外,有的使用者仍然活躍。然後,使用者在04:00 WIB開始活動,在07:00 WIB達到高峰,然後下降,直到12:00 WIB再次上升。
# JOKO WIDODO
df.senti.score.1 = data.frame(table(senti.jokowi$score))
colnames(df.senti.score.1) = c('Score','Freq')
# 資料預處理
df.senti.score.1$Score = as.character(df.senti.score.1$Score)
df.senti.score.1$Score = as.numeric(df.senti.score.1$Score)
Score1 = df.senti.score.1$Score
sign(df.senti.score.1[1,1])
for (i in 1:nrow(df.senti.score.1)) {
sign.row = sign(df.senti.score.1[i,'Score'])
for (j in 1:ncol(df.senti.score.1)) {
df.senti.score.1[i,j] = df.senti.score.1[i,j] * sign.row
}
}
df.senti.score.1$Label = c(letters[1:nrow(df.senti.score.1)])
df.senti.score.1$Sentiment = ifelse(df.senti.score.1$Freq < 0,
'Negative','Positive')
df.senti.score.1$Score1 = Score1
# 資料視覺化
ggplot(df.senti.score.1)+
geom_bar(aes(x = Label,
y = Freq,
fill = Sentiment),
stat = 'identity',
show.legend = FALSE)+
# 積極情感
geom_hline(yintercept = mean(abs(df.senti.score.1[which(df.senti.score.1$Sentiment == 'Positive'),'Freq'])),
col = I('black'),
size = 1)+
geom_text(aes(fontface = 'italic',
label = paste('Average Freq:',
ceiling(mean(abs(df.senti.score.1[which(df.senti.score.1$Sentiment == 'Positive'),'Freq'])))),
x = 10,
y = mean(abs(df.senti.score.1[which(df.senti.score.1$Sentiment == 'Positive'),'Freq']))+30),
hjust = 'right',
size = 4)+
# 消極情感
geom_hline(yintercept = mean(df.senti.score.1[which(df.senti.score.1$Sentiment == 'Negative'),'Freq']),
col = I('black'),
size = 1)+
geom_text(aes(fontface = 'italic',
label = paste('Average Freq:',
ceiling(mean(abs(df.senti.score.1[which(df.senti.score.1$Sentiment == 'Negative'),'Freq'])))),
x = 5,
y = mean(df.senti.score.1[which(df.senti.score.1$Sentiment == 'Negative'),'Freq'])-15),
hjust = 'left',
size = 4)+
labs(title = 'Barplot of Sentiments',
subtitle = 'Joko Widodo',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlab('Score')+
scale_x_discrete(limits = df.senti.score.1$Label,
labels = df.senti.score.1$Score1)+
theme_bw()+
scale_fill_brewer(palette = 'Dark2')
# PRABOWO SUBIANTO
df.senti.score.2 = data.frame(table(senti.prabowo$score))
colnames(df.senti.score.2) = c('Score','Freq')
# 資料預處理
df.senti.score.2$Score = as.character(df.senti.score.2$Score)
df.senti.score.2$Score = as.numeric(df.senti.score.2$Score)
Score2 = df.senti.score.2$Score
sign(df.senti.score.2[1,1])
for (i in 1:nrow(df.senti.score.2)) {
sign.row = sign(df.senti.score.2[i,'Score'])
for (j in 1:ncol(df.senti.score.2)) {
df.senti.score.2[i,j] = df.senti.score.2[i,j] * sign.row
}
}
df.senti.score.2$Label = c(letters[1:nrow(df.senti.score.2)])
df.senti.score.2$Sentiment = ifelse(df.senti.score.2$Freq < 0,
'Negative','Positive')
df.senti.score.2$Score1 = Score2
# 資料視覺化
ggplot(df.senti.score.2)+
geom_bar(aes(x = Label,
y = Freq,
fill = Sentiment),
stat = 'identity',
show.legend = FALSE)+
# 積極情感
geom_hline(yintercept = mean(abs(df.senti.score.2[which(df.senti.score.2$Sentiment == 'Positive'),'Freq'])),
col = I('black'),
size = 1)+
geom_text(aes(fontface = 'italic',
label = paste('Average Freq:',
ceiling(mean(abs(df.senti.score.2[which(df.senti.score.2$Sentiment == 'Positive'),'Freq'])))),
x = 11,
y = mean(abs(df.senti.score.2[which(df.senti.score.2$Sentiment == 'Positive'),'Freq']))+20),
hjust = 'right',
size = 4)+
# 消極情感
geom_hline(yintercept = mean(df.senti.score.2[which(df.senti.score.2$Sentiment == 'Negative'),'Freq']),
col = I('black'),
size = 1)+
geom_text(aes(fontface = 'italic',
label = paste('Average Freq:',
ceiling(mean(abs(df.senti.score.2[which(df.senti.score.2$Sentiment == 'Negative'),'Freq'])))),
x = 9,
y = mean(df.senti.score.2[which(df.senti.score.2$Sentiment == 'Negative'),'Freq'])-10),
hjust = 'left',
size = 4)+
labs(title = 'Barplot of Sentiments',
subtitle = 'Prabowo Subianto',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlab('Score')+
scale_x_discrete(limits = df.senti.score.2$Label,
labels = df.senti.score.2$Score1)+
theme_bw()+
scale_fill_brewer(palette = 'Dark2')
圖4是包含關鍵字“Joko Widodo”和“Prabowo Subianto”的情感得分密度圖。tweets的得分是由組成tweets的詞根的平均得分得到的。因此,它的分數是針對每個詞根給出的,其值介於-10到10之間。如果分數越小,那麼微博中的負面情緒就越多,反之亦然。根據圖4(左),可以得出結論,包含關鍵字“Joko Widodo”的推文的負面情緒在-10到-1之間,中間得分為-4。它也適用於積極的情緒(當然,有一個積極的分數)。根據圖4(左)中的密度圖,我們發現積極情緒的得分具有相當小的方差。因此,我們得出結論,對包含關鍵詞“Joko Widodo”的微博的積極情緒並不是太多樣化。
圖4(右)顯示了包含關鍵字“Prabowo Subianto”的情感得分密度圖。它與圖4(左)不同,因為圖4(右)上的負面情緒在-8到-1之間。這意味著tweets沒有太多負面情緒(tweets有負面情緒,但不夠高)。此外,負面情緒得分的分佈在4和1之間有兩個峰值。然而,積極情緒從1到10不等。與圖4(左)相比,圖4(右)的積極情緒具有較高的方差,在3和10範圍內有兩個峰值。這表明,包含關鍵詞“Prabowo Subianto”的微博具有很高的積極情緒。
# JOKO WIDODO
df.senti.3 = as.data.frame(table(senti.jokowi$class))
colnames(df.senti.3) = c('Sentiment','Freq')
# 資料預處理
df.pie.1 = df.senti.3
df.pie.1$Prop = df.pie.1$Freq/sum(df.pie.1$Freq)
df.pie.1 = df.pie.1 %>%
arrange(desc(Sentiment)) %>%
mutate(lab.ypos = cumsum(Prop) - 0.5*Prop)
# 資料視覺化
ggplot(df.pie.1,
aes(x = 2,
y = Prop,
fill = Sentiment))+
geom_bar(stat = 'identity',
col = 'white',
alpha = 0.75,
show.legend = TRUE)+
coord_polar(theta = 'y',
start = 0)+
geom_text(aes(y = lab.ypos,
label = Prop),
color = 'white',
fontface = 'italic',
size = 4)+
labs(title = 'Piechart of Sentiments',
subtitle = 'Joko Widodo',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlim(c(0.5,2.5))+
theme_void()+
scale_fill_brewer(palette = 'Dark2')+
theme(legend.title = element_blank(),
legend.position = 'right')
# PRABOWO SUBIANTO
df.senti.4 = as.data.frame(table(senti.prabowo$class))
colnames(df.senti.4) = c('Sentiment','Freq')
# 資料預處理
df.pie.2 = df.senti.4
df.pie.2$Prop = df.pie.2$Freq/sum(df.pie.2$Freq)
df.pie.2 = df.pie.2 %>%
arrange(desc(Sentiment)) %>%
mutate(lab.ypos = cumsum(Prop) - 0.5*Prop)
# 資料視覺化
ggplot(df.pie.2,
aes(x = 2,
y = Prop,
fill = Sentiment))+
geom_bar(stat = 'identity',
col = 'white',
alpha = 0.75,
show.legend = TRUE)+
coord_polar(theta = 'y',
start = 0)+
geom_text(aes(y = lab.ypos,
label = Prop),
color = 'white',
fontface = 'italic',
size = 4)+
labs(title = 'Piechart of Sentiments',
subtitle = 'Prabowo Subianto',
caption = 'Twitter Crawling 28 - 29 May 2019')+
xlim(c(0.5,2.5))+
theme_void()+
scale_fill_brewer(palette = 'Dark2')+
theme(legend.title = element_blank(),
legend.position = 'right')
圖5是推特的情緒得分彙總,這些微博被分為負面情緒、中性情緒和積極情緒。消極情緒是指得分低於零的情緒,中性是指分數等於零的情緒,積極情緒得分大於零。從圖5可以看出,關鍵字為“Joko Widodo”的微博的負面情緒百分比低於關鍵字為“Prabowo Subianto”的tweet。有6.3%的差異。研究還發現,與關鍵詞為Prabowo Subianto的微博相比,包含關鍵詞“Joko Widodo”的微博具有更高的中性情緒和積極情緒。通過piechart的研究發現,與關鍵字為“Prabowo Subianto”的tweet相比,帶有關鍵字“Joko Widodo”的tweet傾向於擁有更高比例的積極情緒。但是通過密度圖發現,積極和消極情緒得分的分佈表明,與“Joko Widodo”相比,包含關鍵字“Prabowo Subianto”的微博往往具有更高的情緒得分。它必須進行進一步的分析。
圖6顯示了使用者在2019年5月28-29日經常上傳的tweet(關鍵詞“Joko Widodo”和“Prabowo Subianto”)中的術語或單詞。通過這個WordCloud視覺化,可以找到熱門話題,這些話題都是針對關鍵詞進行討論的。對於包含關鍵詞“Joko Widodo”的tweet,我們發現術語“tuang”、“petisi”、“negara”、“aman”和“nusantara”是前五名,每個tweet出現的次數最多。然而,包含關鍵詞“Joko Widodo”的tweet發現,“Prabowo”、“Subianto”、“kriminalisasi”、“selamat”和“dubai”是每個tweet中出現次數最多的前五個詞。這間接地顯示了以關鍵字“Prabowo Subianto”上傳的tweet的模式,即:幾乎可以肯定的是,每個上傳的tweet都直接包含“Prabowo Subianto”的名稱,而不是通過提及(@)。這是因為,在文字預處理中,提到(@)已被刪除。
可以前往我的GitHub repo查詢程式碼:https://github.com/audhiaprilliant/Indonesia-Public-Election-Twitter-Sentiment-Analysis
參考引用
[1] K. Borau, C. Ullrich, J. Feng, R. Shen. Microblogging for Language Learning: Using Twitter to Train Communicative and Cultural Competence (2009), Advances in Web-Based Learning — ICWL 2009, 8th International Conference, Aachen, Germany, August 19–21, 2009.
原文連結:https://towardsdatascience.com/twitter-data-visualization-fb4f45b63728
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