Twitter (and Sentiment) Analysis of Donald Trump’s Tweets using an API

In this project, I cleaned and analyzed data collected from Donald Trump’s Twitter account using an API.

Loading libraries

library(dplyr)
library(twitteR)
library(ROAuth)
library(ggplot2)
library(RColorBrewer)
library(wordcloud)

Retrieving 3200 most recent tweets by Donald Trump

consumer_key = "s8OWnvqLPckxI0JBcrnZR6rLM"
consumer_secret = "Zve0oxqK7PuU3MIwN0Snz2wvLWmK100hZ2hxczKGMmqxAo0oZe"
access_token = "111199907-Jpm02lPlAPXk3tenVitYPH2TObjVcZSz3AOu7qGs"
access_secret ="616o99du0NZt3bGrHYQBJ8SWOb3V7QTAZdEVDYk6ajQRl"
requestURL = "https://api.twitter.com/oauth/request_token"
accessURL ="https://api.twitter.com/oauth/access_token"
authURL ="https://api.twitter.com/oauth/authorize"
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

[1] "Using direct authentication"

tw=userTimeline("realDonaldTrump", n=3000, includeRts=TRUE)
df=twListToDF(tw)

What is his most commonly used platform to tweet?

Tweets from his account are mostly from an iPhone

df$statusSource = substr(df$statusSource, regexpr(">", df$statusSource) + 1, regexpr("</a>", df$statusSource) -  1)
ggplot(df, aes(x=statusSource), fill()) +
  geom_bar() +
  labs(title="Twitter platforms by @realDonaldTrump") +
  ylab(label="Number of tweets") +
  xlab(label="Type of platform")

What time of the day does he usually tweet?

He is most likely to tweet during the afternoon (11am-3pm) and less during Tuesdays and Thursdays.

df$timeonly = as.numeric(difftime(df$created, trunc(df$created, "days"), attr(df$created, "tzone"), "hours"))
ggplot(df, aes(x=timeonly)) +
  geom_histogram(aes(y=..density..), bins=35, colour="#F8766D", fill="#F8766D") +
  geom_density() +
  labs(title="Tweets by time of day by @realDonaldTrump") +
  xlab(label="Hour of day")


ggplot(as.data.frame(table(weekdays(df[,"created"]))[c("Monday", "Tuesday","Wednesday", "Thursday", "Friday","Saturday", "Sunday")])) +
  geom_bar(aes(x=Var1, y=Freq), stat="identity") +
  labs(title="Tweet frequency by day by @realDonaldTrump") +
  xlab(label="Days of the week") +
  ylab(label="Frequency")

What are the most frequently used words in his tweets?

After removing characters that are not letters, additional spaces between words, changing all words to lower-case etc.., the wordcloud shows that the two most frequent words in his tweets are “great” and “president”

Words2Use = unlist(strsplit(df$text," "))
sentence1 = tolower(Words2Use)
sentence1_5 = gsub("amp", " ", sentence1)
sentence1_6 = gsub("rt", " ", sentence1_5)
sentence2 = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", " ", sentence1_6)
sentence3 = gsub("@\\w+", " ", sentence2)
sentence4 = gsub("(?!')[[:punct:]]", "", sentence3,perl=T)
sentence5 = gsub("[[:cntrl:]]", "", sentence4)
sentence6 = gsub("[[:digit:]]", "", sentence5)
sentence7 = iconv(sentence5, "ASCII", "UTF-8", sub = "") 
sentence9 = gsub("http\\w+", "", sentence7)
sentence10 = gsub("[ \t]{2,}", " ", sentence9)
sentence11 = gsub("^\\s+|\\s+$", "", sentence10) 
word.list = strsplit(sentence11, " ")
words = unlist(word.list)
NotStopWords = words[!words %in% tm::stopwords(kind = "english")]
tbl = table(NotStopWords)
layout(matrix(c(1, 2), nrow=2), heights=c(1, 4))
par(mar=rep(0, 4))
plot.new()
text(x=0.5, y=0.5, "Commonly used words from tweets by @realDonaldTrump" )

wordcloud(names(sort(tbl,decreasing=TRUE)[1:40]),sort(tbl,decreasing=TRUE)[1:50],
          colors = rainbow(9), min.freq = 100)


tbldf=as.data.frame(sort(tbl,decreasing=TRUE)[1:25])
tbldf %>%
  filter(Freq>10) -> tbldf2
ggplot(tbldf2, aes(x=reorder(NotStopWords, Freq), y=Freq)) +
  geom_bar(stat="identity") +
  coord_flip() + 
  ylab(label="Occurences") +
  xlab(label="Word") +
  labs(title="Frequently used words in tweets by @realDonaldTrump")

Sentiment Analysis

The first graph shows a sentiment analysis of his tweets during a period of time. The second graph shows that the sentiment scores for his tweets follow a normal distribution, with an average score of 0.2304609 which is close to neutral. The list of positive and negative words were obtained from https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html.

pos = scan("positive-words.txt", what = "character",comment.char = ";")

Read 2006 items

neg = scan("negative-words.txt", what = "character",comment.char = ";")

Read 4783 items

getSentimentScore = function(tweet_text, pos, neg) {

  sentence = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", tweet_text)

  sentence = gsub("@\\w+", "", sentence)

  sentence = gsub("[[:punct:]]", "", sentence)

  sentence = gsub("[[:cntrl:]]", "", sentence)

  sentence = gsub("[[:digit:]]", "", sentence) 

  sentence = gsub("http\\w+", "", sentence)

  sentence = gsub("^\\s+|\\s+$", "", sentence)

  sentence = iconv(sentence, "UTF-8", "ASCII", sub = "")
  sentence = tolower(sentence)

  word.list = strsplit(sentence, " ")

  score = numeric(length(word.list)) # loop through each tweet
  positive = numeric(length(word.list))
  negative = numeric(length(word.list))
  for (i in 1:length(word.list)) {

    pos.matches = match(word.list[[i]], pos) 
    neg.matches = match(word.list[[i]], neg)

    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)

    score[i] = sum(pos.matches) - sum(neg.matches)
    positive[i] = sum(pos.matches)
    negative[i] = sum(neg.matches)
  }
  return(data.frame(positive_score = positive, 
                    negative_score = negative,
                    sentiment_score = score))
}
output = getSentimentScore(df$text, pos, neg)
df$sentiment = output[, "sentiment_score"]
df$pos_sentiment = output[, "positive_score"]
df$neg_sentiment = output[, "negative_score"]
df$day = as.Date(cut(df$created, breaks = "day"))
df %>% 
  group_by(day) %>%
  summarise(meanPos = mean(pos_sentiment), meanNeg = mean(neg_sentiment), meanSent = mean(sentiment)) -> sentiment_byday

ggplot(sentiment_byday,aes(x=day)) +
  geom_line(aes(y=meanNeg,colour="Positive words")) +
  geom_line(aes(y=meanPos, colour="Negative words")) +
  scale_colour_manual(values=c(`Positive words`="#619CFF", `Negative words`="#F8766D")) +
  theme(legend.justification = c(1, 1), legend.position = c(.95, .95), legend.title=element_blank()) +
  xlab(label="Date") +
  ylab(label="Sentiment Score")


ggplot(output, aes(x=sentiment_score)) +
  geom_density(bw=.5)  +
  labs(title="Distribution of sentiment scores of tweets by @realDonaldTrump") +
  geom_vline(xintercept = mean(output$sentiment_score), color = "red", linetype = "dashed") +
  xlab(label="Sentiment Score") +
  ylab(label="Density")

References

; Minqing Hu and Bing Liu. “Mining and Summarizing Customer Reviews.” ; Proceedings of the ACM SIGKDD International Conference on Knowledge ; Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, ; Washington, USA, ; Bing Liu, Minqing Hu and Junsheng Cheng. “Opinion Observer: Analyzing ; and Comparing Opinions on the Web.” Proceedings of the 14th ; International World Wide Web conference (WWW-2005), May 10-14, ; 2005, Chiba, Japan.

---
title: "Twitter (and Sentiment) Analysis of Donald Trump's Tweets using an API"
Author: "Alexander Lo"
output:
  html_notebook: default
  html_document: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
##### In this project, I cleaned and analyzed data collected from Donald Trump's Twitter account using an API.

##### Loading libraries
```{r}
library(dplyr)
library(twitteR)
library(ROAuth)
library(ggplot2)
library(RColorBrewer)
library(wordcloud)
```

##### Retrieving 3200 most recent tweets by Donald Trump
```{r}
consumer_key = "s8OWnvqLPckxI0JBcrnZR6rLM"
consumer_secret = "Zve0oxqK7PuU3MIwN0Snz2wvLWmK100hZ2hxczKGMmqxAo0oZe"
access_token = "111199907-Jpm02lPlAPXk3tenVitYPH2TObjVcZSz3AOu7qGs"
access_secret ="616o99du0NZt3bGrHYQBJ8SWOb3V7QTAZdEVDYk6ajQRl"
requestURL = "https://api.twitter.com/oauth/request_token"
accessURL ="https://api.twitter.com/oauth/access_token"
authURL ="https://api.twitter.com/oauth/authorize"
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)
tw=userTimeline("realDonaldTrump", n=3000, includeRts=TRUE)
df=twListToDF(tw)
```

##### What is his most commonly used platform to tweet? 
Tweets from his account are mostly from an iPhone
```{r}
df$statusSource = substr(df$statusSource, regexpr(">", df$statusSource) + 1, regexpr("</a>", df$statusSource) -  1)
ggplot(df, aes(x=statusSource), fill()) +
  geom_bar() +
  labs(title="Twitter platforms by @realDonaldTrump") +
  ylab(label="Number of tweets") +
  xlab(label="Type of platform")
```

##### What time of the day does he usually tweet? 
He is most likely to tweet during the afternoon (11am-3pm) and less during Tuesdays and Thursdays.
```{r}
df$timeonly = as.numeric(difftime(df$created, trunc(df$created, "days"), attr(df$created, "tzone"), "hours"))
ggplot(df, aes(x=timeonly)) +
  geom_histogram(aes(y=..density..), bins=35, colour="#F8766D", fill="#F8766D") +
  geom_density() +
  labs(title="Tweets by time of day by @realDonaldTrump") +
  xlab(label="Hour of day") 

ggplot(as.data.frame(table(weekdays(df[,"created"]))[c("Monday", "Tuesday","Wednesday", "Thursday", "Friday","Saturday", "Sunday")])) +
  geom_bar(aes(x=Var1, y=Freq), stat="identity") +
  labs(title="Tweet frequency by day by @realDonaldTrump") +
  xlab(label="Days of the week") +
  ylab(label="Frequency")
```

##### What are the most frequently used words in his tweets?
After removing characters that are not letters, additional spaces between words, changing all words to lower-case etc.., the wordcloud shows that the two most frequent words in his tweets are "great" and "president"
```{r}
Words2Use = unlist(strsplit(df$text," "))
sentence1 = tolower(Words2Use)
sentence1_5 = gsub("amp", " ", sentence1)
sentence1_6 = gsub("rt", " ", sentence1_5)
sentence2 = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", " ", sentence1_6)
sentence3 = gsub("@\\w+", " ", sentence2)
sentence4 = gsub("(?!')[[:punct:]]", "", sentence3,perl=T)
sentence5 = gsub("[[:cntrl:]]", "", sentence4)
sentence6 = gsub("[[:digit:]]", "", sentence5)
sentence7 = iconv(sentence5, "ASCII", "UTF-8", sub = "") 
sentence9 = gsub("http\\w+", "", sentence7)
sentence10 = gsub("[ \t]{2,}", " ", sentence9)
sentence11 = gsub("^\\s+|\\s+$", "", sentence10) 
word.list = strsplit(sentence11, " ")
words = unlist(word.list)
NotStopWords = words[!words %in% tm::stopwords(kind = "english")]
tbl = table(NotStopWords)
layout(matrix(c(1, 2), nrow=2), heights=c(1, 4))
par(mar=rep(0, 4))
plot.new()
text(x=0.5, y=0.5, "Commonly used words from tweets by @realDonaldTrump" )
wordcloud(names(sort(tbl,decreasing=TRUE)[1:40]),sort(tbl,decreasing=TRUE)[1:50],
          colors = rainbow(9), min.freq = 100)

tbldf=as.data.frame(sort(tbl,decreasing=TRUE)[1:25])
tbldf %>%
  filter(Freq>10) -> tbldf2
ggplot(tbldf2, aes(x=reorder(NotStopWords, Freq), y=Freq)) +
  geom_bar(stat="identity") +
  coord_flip() + 
  ylab(label="Occurences") +
  xlab(label="Word") +
  labs(title="Frequently used words in tweets by @realDonaldTrump")
```

##### Sentiment Analysis
The first graph shows a sentiment analysis of his tweets during a period of time. The second graph shows that the sentiment scores for his tweets follow a normal distribution, with an average score of 0.2304609 which is close to neutral. The list of positive and negative words were obtained from https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html. 
```{r}
pos = scan("positive-words.txt", what = "character",comment.char = ";")
neg = scan("negative-words.txt", what = "character",comment.char = ";")
getSentimentScore = function(tweet_text, pos, neg) {

  sentence = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", tweet_text)

  sentence = gsub("@\\w+", "", sentence)

  sentence = gsub("[[:punct:]]", "", sentence)

  sentence = gsub("[[:cntrl:]]", "", sentence)

  sentence = gsub("[[:digit:]]", "", sentence) 

  sentence = gsub("http\\w+", "", sentence)

  sentence = gsub("^\\s+|\\s+$", "", sentence)

  sentence = iconv(sentence, "UTF-8", "ASCII", sub = "")
  sentence = tolower(sentence)

  word.list = strsplit(sentence, " ")

  score = numeric(length(word.list)) # loop through each tweet
  positive = numeric(length(word.list))
  negative = numeric(length(word.list))
  for (i in 1:length(word.list)) {

    pos.matches = match(word.list[[i]], pos) 
    neg.matches = match(word.list[[i]], neg)

    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)

    score[i] = sum(pos.matches) - sum(neg.matches)
    positive[i] = sum(pos.matches)
    negative[i] = sum(neg.matches)
  }
  return(data.frame(positive_score = positive, 
                    negative_score = negative,
                    sentiment_score = score))
}
output = getSentimentScore(df$text, pos, neg)
df$sentiment = output[, "sentiment_score"]
df$pos_sentiment = output[, "positive_score"]
df$neg_sentiment = output[, "negative_score"]
df$day = as.Date(cut(df$created, breaks = "day"))
df %>% 
  group_by(day) %>%
  summarise(meanPos = mean(pos_sentiment), meanNeg = mean(neg_sentiment), meanSent = mean(sentiment)) -> sentiment_byday

ggplot(sentiment_byday,aes(x=day)) +
  geom_line(aes(y=meanNeg,colour="Positive words")) +
  geom_line(aes(y=meanPos, colour="Negative words")) +
  scale_colour_manual(values=c(`Positive words`="#619CFF", `Negative words`="#F8766D")) +
  theme(legend.justification = c(1, 1), legend.position = c(.95, .95), legend.title=element_blank()) +
  xlab(label="Date") +
  ylab(label="Sentiment Score")

ggplot(output, aes(x=sentiment_score)) +
  geom_density(bw=.5)  +
  labs(title="Distribution of sentiment scores of tweets by @realDonaldTrump") +
  geom_vline(xintercept = mean(output$sentiment_score), color = "red", linetype = "dashed") +
  xlab(label="Sentiment Score") +
  ylab(label="Density")
```

##### References
;   Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." 
;       Proceedings of the ACM SIGKDD International Conference on Knowledge 
;       Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, 
;       Washington, USA, 
;   Bing Liu, Minqing Hu and Junsheng Cheng. "Opinion Observer: Analyzing 
;       and Comparing Opinions on the Web." Proceedings of the 14th 
;       International World Wide Web conference (WWW-2005), May 10-14, 
;       2005, Chiba, Japan.
