关于人工智能:R语言进行Twitter数据可视化

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作者 |Audhi Aprilliant
编译 |VK
起源 |Towards Datas Science

概述

对于这个我的项目，咱们在 2019 年 5 月 28-29 日通过爬虫来应用 Twitter 的原始数据。此外，数据是 CSV 格局（逗号分隔），能够在这里下载。

https://github.com/audhiapril…

它波及两个主题，一个是蕴含关键字“Joko Widodo”的 Joko Widodo 的数据，另一个是带有关键字“Prabowo Subianto”的 Prabowo Subianto 的数据。其中包含几个变量和信息，以确定用户情绪。实际上，数据有 16 个变量或属性和 1000 多个察看值。表 1 列出了一些变量。

# 导入库
library(ggplot2)
library(lubridate)

# 加载 Joko Widodo 的数据
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 = ',')
                        
# 加载 Prabowo Subianto 的数据
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 = ',')

数据可视化

数据摸索旨在从 Twitter 数据中获取任何信息。应该指出的是，数据曾经进行了文本预处理。咱们对那些被认为是很乏味的变量进行摸索。。

# TWEETS 的条形图 -JOKO WIDODO
data.jokowi.df$created = ymd_hms(data.jokowi.df$created,
                                 tz = 'Asia/Jakarta')
# 另一种制作“date”和“hour”变量的办法
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$hour))
colnames(data.hour.date1) = c('Hour','Total.Tweets')
# 创立数据可视化
ggplot(data.hour.date1)+
  geom_bar(aes(x = Hour,
               y = Total.Tweets,
               fill = I('blue')),
           stat = 'identity',
           alpha = 0.75,
           show.legend = FALSE)+
  geom_hline(yintercept = mean(data.hour.date1$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 的条形图 -PRABOWO SUBIANTO
data.prabowo.df$created = ymd_hms(data.prabowo.df$created,
                                  tz = 'Asia/Jakarta')
                                  
# 另一种制作“date”和“hour”变量的办法
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)

# 数据预处理
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)

# 数据可视化
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')

依据图 1，咱们能够得出结论，通过数据抓取（关键字“Jokow Widodo”和“Prabowo Subianto”）失去的 tweet 数量并不类似，即便在同一日期。

例如，在图 1（左）中，从视觉上看，对于关键字为“Joko Widodo”的推文，仅在 2019 年 5 月 28 日 03:00–17:00 WIB 期间取得。而在图 1（右图）中，咱们得出的论断是，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）期间取得的关键词为“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')

依据图 2，咱们失去了应用关键字“Joko Widodo”和“Prabowo Subianto”的用户之间的显著差别。关键词为“Joko Widodo”的 tweet 在某个特定工夫（07:00–09:00 WIB）议论 Joko Widodo 往往十分强烈，08:00 WIB 的 tweet 数量最多。它有 348 条推文。然而，在 2019 年 5 月 28 日至 29 日期间，关键词为“Prabowo Subianto”的推文往往会一直地议论 Prabowo Subianto。2019 年 5 月 28 日至 29 日，每小时上传关键词为“Prabowo Subianto”的推文均匀为 36 条。

# 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())

图 3 是 2019 年 5 月 28 日至 29 日以“Joko Widodo”和“Prabowo Subianto”为关键词的多条推文的条形图。由图 3(左) 能够得出，Twitter 用户在 19:00-23:59 WIB 上议论 Prabowo Subianto 的频率较低。这是因为印尼人的休息时间造成的。然而，这些带有主题的推文总是在午夜更新，因为有的用户寓居在国外，有的用户依然沉闷。而后，用户在 04:00 WIB 开始流动，在 07:00 WIB 达到顶峰，而后降落，直到 12:00 WIB 再次回升。

# JOKO WIDODO
df.senti.score.1 = data.frame(table(senti.jokowi$score))
colnames(df.senti.score.1) = c('Score','Freq')
# 数据预处理
df.senti.score.1$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
# 数据可视化
ggplot(df.senti.score.1)+
  geom_bar(aes(x = Label,
               y = Freq,
               fill = Sentiment),
           stat = 'identity',
           show.legend = FALSE)+
  # 踊跃情感
  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$Sentiment == 'Positive'),'Freq']))+30),
            hjust = 'right',
            size = 4)+
  # 消极情感
  geom_hline(yintercept = mean(df.senti.score.1[which(df.senti.score.1$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$score))
colnames(df.senti.score.2) = c('Score','Freq')
# 数据预处理
df.senti.score.2$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
# 数据可视化
ggplot(df.senti.score.2)+
  geom_bar(aes(x = Label,
               y = Freq,
               fill = Sentiment),
           stat = 'identity',
           show.legend = FALSE)+
  # 踊跃情感
  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$Sentiment == 'Positive'),'Freq']))+20),
            hjust = 'right',
            size = 4)+
  # 消极情感
  geom_hline(yintercept = mean(df.senti.score.2[which(df.senti.score.2$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')

图 4 是蕴含关键字“Joko Widodo”和“Prabowo Subianto”的情感得分密度图。tweets 的得分是由组成 tweets 的词根的均匀得分失去的。因而，它的分数是针对每个词根给出的，其值介于 -10 到 10 之间。如果分数越小，那么微博中的负面情绪就越多，反之亦然。依据图 4（左），能够得出结论，蕴含关键字“Joko Widodo”的推文的负面情绪在 -10 到 - 1 之间，两头得分为 -4。它也实用于踊跃的情绪（当然，有一个踊跃的分数）。依据图 4（左）中的密度图，咱们发现积极情绪的得分具备相当小的方差。因而，咱们得出结论，对蕴含关键词“Joko Widodo”的微博的积极情绪并不是太多样化。

图 4（右）显示了蕴含关键字“Prabowo Subianto”的情感得分密度图。它与图 4（左）不同，因为图 4（右）上的负面情绪在 - 8 到 - 1 之间。这意味着 tweets 没有太多负面情绪（tweets 有负面情绪，但不够高）。此外，负面情绪得分的散布在 4 和 1 之间有两个峰值。然而，积极情绪从 1 到 10 不等。与图 4（左）相比，图 4（右）的积极情绪具备较高的方差，在 3 和 10 范畴内有两个峰值。这表明，蕴含关键词“Prabowo Subianto”的微博具备很高的积极情绪。

# JOKO WIDODO
df.senti.3 = as.data.frame(table(senti.jokowi$class))
colnames(df.senti.3) = c('Sentiment','Freq')
# 数据预处理
df.pie.1 = df.senti.3
df.pie.1$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)
# 数据可视化
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$class))
colnames(df.senti.4) = c('Sentiment','Freq')
# 数据预处理
df.pie.2 = df.senti.4
df.pie.2$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)
# 数据可视化
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')

图 5 是推特的情绪得分汇总，这些微博被分为负面情绪、中性情绪和积极情绪。消极情绪是指得分低于零的情绪，中性是指分数等于零的情绪，积极情绪得分大于零。从图 5 能够看出，关键字为“Joko Widodo”的微博的负面情绪百分比低于关键字为“Prabowo Subianto”的 tweet。有 6.3% 的差别。钻研还发现，与关键词为 Prabowo Subianto 的微博相比，蕴含关键词“Joko Widodo”的微博具备更高的中性情绪和积极情绪。通过 piechart 的钻研发现，与关键字为“Prabowo Subianto”的 tweet 相比，带有关键字“Joko Widodo”的 tweet 偏向于领有更高比例的积极情绪。然而通过密度图发现，踊跃和消极情绪得分的散布表明，与“Joko Widodo”相比，蕴含关键字“Prabowo Subianto”的微博往往具备更高的情绪得分。它必须进行进一步的剖析。

图 6 显示了用户在 2019 年 5 月 28-29 日常常上传的 tweet（关键词“Joko Widodo”和“Prabowo Subianto”）中的术语或单词。通过这个 WordCloud 可视化，能够找到热门话题，这些话题都是针对关键词进行探讨的。对于蕴含关键词“Joko Widodo”的 tweet，咱们发现术语“tuang”、“petisi”、“negara”、“aman”和“nusantara”是前五名，每个 tweet 呈现的次数最多。然而，蕴含关键词“Joko Widodo”的 tweet 发现，“Prabowo”、“Subianto”、“kriminalisasi”、“selamat”和“dubai”是每个 tweet 中呈现次数最多的前五个词。这间接地显示了以关键字“Prabowo Subianto”上传的 tweet 的模式，即：简直能够必定的是，每个上传的 tweet 都间接蕴含“Prabowo Subianto”的名称，而不是通过提及（@）。这是因为，在文本预处理中，提到（@）已被删除。

能够返回我的 GitHub repo 查找代码：https://github.com/audhiapril…

参考援用

[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.

原文链接：https://towardsdatascience.co…

欢送关注磐创 AI 博客站：
http://panchuang.net/

sklearn 机器学习中文官网文档：
http://sklearn123.com/

欢送关注磐创博客资源汇总站：
http://docs.panchuang.net/

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人工智能
发表至： 人工智能
2020-10-18
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