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<a href="Task-1.html">Task 1</a>
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<h1 class="title toc-ignore">Sentiment Analysis between Donald Trump and
Hillary Clinton</h1>
</div>
<p><br></p>
<p>This task has two parts.</p>
<p>In the first part, we will use a dataset called <a
href="resources/tweets.csv">tweets.csv</a> that contains tweets and
comments from Donald Trump and Hillary Clinton. We will analyze the
sentiments in these tweets and comments. We will categorize the tweets
and comments as positive, negative, or neutral and create visualizations
to show the results.</p>
<p>In the second part, we will collect 40 journal articles about the
sentiments around Hillary Clinton and Donald Trump. Then, we will
analyze these articles to understand how sentiment analysis was used and
what trends were found in public opinion. Here’s the folder containing
all of those collected articles that published to Google Scholar: <a
href="https://muhammad-zulfikar.github.io/bigDataInInternationalRelations/resources/pdf/">PDF
folder</a></p>
<p><br></p>
<div id="sentiment-analysis-from-tweets" class="section level1">
<h1>Sentiment Analysis from Tweets</h1>
<p><br></p>
<div id="load-required-libraries" class="section level2">
<h2>Load required libraries</h2>
<pre class="r"><code># Load required libraries
library(readr)
library(dplyr)
library(stringr)
library(tidytext)
library(ggplot2)</code></pre>
<p><br></p>
</div>
<div id="read-the-csv-file" class="section level2">
<h2>Read the CSV file</h2>
<pre class="r"><code># Read the CSV file
tweets <- read_csv("./resources/tweets.csv")</code></pre>
<p><br></p>
</div>
<div id="data-cleaning-and-preprocessing" class="section level2">
<h2>Data cleaning and preprocessing</h2>
<pre class="r"><code># Data cleaning and preprocessing
tweets <- tweets %>%
mutate(text = str_replace_all(text, "https?://\\S+", "")) %>% # Remove URLs
mutate(text = str_replace_all(text, "@\\w+", "")) %>% # Remove mentions
mutate(text = str_replace_all(text, "#\\w+", "")) %>% # Remove hashtags
mutate(text = str_replace_all(text, "[^[:alnum:][:space:]]+", "")) %>% # Remove special characters
mutate(text = str_trim(text)) %>% # Trim whitespace
filter(!is.na(text) & text != "") # Remove empty/NA rows</code></pre>
<pre class="r"><code># Tokenize text data
tweets_tokens <- tweets %>%
unnest_tokens(word, text)</code></pre>
<p><br></p>
</div>
<div id="sentiment-analysis" class="section level2">
<h2>Sentiment Analysis</h2>
<pre class="r"><code># Perform sentiment analysis
sentiment_scores <- tweets_tokens %>%
inner_join(get_sentiments("afinn"), by = "word") %>%
group_by(handle) %>%
summarize(sentiment_score = sum(value))</code></pre>
<pre class="r"><code># Plot sentiment analysis graph
ggplot(sentiment_scores, aes(x = handle, y = sentiment_score, fill = handle)) +
geom_bar(stat = "identity") +
labs(title = "Sentiment Analysis between Donald Trump and Hillary Clinton",
x = "Candidate",
y = "Sentiment Score") +
theme_minimal() +
theme(legend.position = "none")</code></pre>
<p><img src="Task-2_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
<p><br></p>
</div>
</div>
<div id="sentiment-analysis-from-journalarticle" class="section level1">
<h1>Sentiment Analysis from Journal/Article</h1>
<p><br></p>
<div id="data-scraping" class="section level2">
<h2>Data Scraping</h2>
<p><br></p>
<div id="load-necessary-libraries" class="section level3">
<h3>Load necessary libraries</h3>
<pre class="r"><code># Load necessary libraries
library(rvest)
library(httr)
library(tools)</code></pre>
<p><br></p>
</div>
<div id="scrape-journalarticle-pdfs-from-google-scholar"
class="section level3">
<h3>Scrape journal/article PDFs from Google Scholar</h3>
<pre class="r"><code># Function to scrape Google Scholar and download PDF files
scrape_google_scholar <- function(query, pages, output_dir) {
# Create output directories if they do not exist
if (!dir.exists(output_dir)) {
dir.create(output_dir, recursive = TRUE)
}
# File to store metadata
metadata_file <- file.path(output_dir, "pdf_metadata.csv")
# Initialize metadata storage
metadata <- data.frame(Link = character(), Title = character(), stringsAsFactors = FALSE)
# List to track downloaded PDF links
downloaded_links <- c()
file_counter <- 1
# Loop through each page
for (i in seq(0, (pages - 1) * 10, by = 10)) {
# Construct the URL
url <- paste0("https://scholar.google.com/scholar?start=", i, "&q=", query, "&hl=en&as_sdt=0,5")
# Read the page content
page <- read_html(url)
# Extract PDF links
links <- page %>% html_nodes("a") %>% html_attr("href")
# Filter PDF links
pdf_links <- links[grepl("\\.pdf$", links)]
# Download each PDF if not already downloaded
for (pdf_link in pdf_links) {
if (!(pdf_link %in% downloaded_links)) {
safe_name <- as.character(file_counter)
pdf_file <- file.path(output_dir, paste0(safe_name, ".pdf"))
tryCatch({
download.file(pdf_link, pdf_file, mode = "wb")
# Append metadata
metadata <- rbind(metadata, data.frame(Link = pdf_link, Title = safe_name, stringsAsFactors = FALSE))
downloaded_links <- c(downloaded_links, pdf_link)
file_counter <- file_counter + 1
}, error = function(e) {
message("Failed to download ", pdf_link, ": ", e)
})
} else {
message("Skipping already downloaded PDF: ", pdf_link)
}
}
}
# Write metadata to CSV
write.csv(metadata, metadata_file, row.names = FALSE)
}</code></pre>
<pre class="r"><code># Run the function
# scrape_google_scholar("donald+trump", 20, "resources/pdf/donald+trump")</code></pre>
<pre class="r"><code># Run the function
# scrape_google_scholar("hillary+clinton", 20, "resources/pdf/hillary+clinton")</code></pre>
<p><br></p>
</div>
<div id="output" class="section level3">
<h3>Output</h3>
<p>We collected 40 Journal/Article PDFs about Donald Trump and Hillary
Clinton</p>
<pre><code>pdf/
└── donald+trump/
├── <filename_1>.pdf
├── <filename_2>.pdf
├── ...
└── hillary+clinton/
├── <filename_1>.pdf
├── <filename_2>.pdf
├── ...</code></pre>
<p>The full collected pdf is available <a
href="https://muhammad-zulfikar.github.io/bigDataInInternationalRelations/resources/pdf/">here</a></p>
<p><br></p>
</div>
</div>
<div id="data-cleaning" class="section level2">
<h2>Data Cleaning</h2>
<p><br></p>
<div id="load-required-libraries-1" class="section level3">
<h3>Load required libraries</h3>
<pre class="r"><code># Load required libraries
library(tidyverse)
library(pdftools)
library(tidytext)
library(ggplot2)</code></pre>
<p><br></p>
</div>
<div id="read-text-from-scraped-pdfs" class="section level3">
<h3>Read text from scraped PDFs</h3>
<pre class="r"><code># Set directory containing PDFs
pdf_dir <- "resources/pdf"
# List all PDF files in the directory
pdf_files <- list.files(pdf_dir, full.names = TRUE)
# Read the PDF files into a data frame
text_data <- lapply(pdf_files, function(file) {
tryCatch(
{
pdf_text(file)
},
error = function(e) {
warning(paste("Error reading file:", file))
return(NA)
}
)
}) %>%
unlist() %>%
na.omit() %>%
data.frame(text = .)</code></pre>
<p><br></p>
</div>
<div id="cleaning-the-text-data" class="section level3">
<h3>Cleaning the text data</h3>
<pre class="r"><code># Function to clean text data
clean_text <- function(text) {
# Convert to lowercase
text <- tolower(text)
# Remove punctuation
text <- gsub("[[:punct:]]", " ", text)
# Remove numbers
text <- gsub("[[:digit:]]", "", text)
# Remove extra white spaces
text <- gsub("\\s+", " ", text)
# Remove stop words
text <- removeWords(text, stopwords("en"))
return(text)
}
# Apply text cleaning function to the text data
text_data <- text_data %>%
mutate(cleaned_text = map_chr(text, clean_text))</code></pre>
<p><br></p>
</div>
</div>
<div id="sentiment-analysis-visualization" class="section level2">
<h2>Sentiment Analysis Visualization</h2>
<pre class="r"><code># Perform sentiment analysis for each candidate
sentiment_analysis <- text_data %>%
mutate(candidate = case_when(
str_detect(cleaned_text, "donald trump") ~ "Trump",
str_detect(cleaned_text, "hillary clinton") ~ "Clinton",
TRUE ~ "Other"
)) %>%
filter(candidate != "Other") %>%
unnest_tokens(word, cleaned_text) %>%
inner_join(get_sentiments("bing"), by = c("word" = "word")) %>%
group_by(candidate) %>%
summarise(sentiment_score = sum(sentiment == "positive") - sum(sentiment == "negative"))</code></pre>
<pre class="r"><code># Plotting the sentiment analysis results
ggplot(sentiment_analysis, aes(x = candidate, y = sentiment_score, fill = candidate)) +
geom_bar(stat = "identity") +
labs(x = "Candidate", y = "Sentiment Score", title = "Sentiment Analysis for Trump vs Clinton") +
theme_minimal() +
scale_fill_manual(values = c("Trump" = "red", "Clinton" = "blue")) +
geom_text(aes(label = sentiment_score), vjust = -0.5)</code></pre>
<p><img src="Task-2_files/figure-html/unnamed-chunk-15-1.png" width="672" /></p>
<p><br></p>
</div>
</div>
<div id="commentary" class="section level1">
<h1>Commentary</h1>
<p><br></p>
<div id="analysis-based-on-tweets" class="section level3">
<h3>Analysis Based on Tweets</h3>
<p>The first bar chart shows the sentiment analysis scores for tweets
mentioning Donald Trump and Hillary Clinton.</p>
<div id="sentiment-scores" class="section level4">
<h4>Sentiment Scores</h4>
<ul>
<li>Donald Trump: The sentiment score is significantly higher, around
3500.</li>
<li>Hillary Clinton: The sentiment score is lower, around 1500.</li>
</ul>
</div>
<div id="interpretation" class="section level4">
<h4>Interpretation</h4>
<ul>
<li>Higher Sentiment Score for Trump: This could indicate that tweets
mentioning Donald Trump generally have a more positive sentiment
compared to those mentioning Hillary Clinton. It’s also possible that
Trump has a larger volume of tweets which could influence the overall
sentiment score.</li>
<li>Lower Sentiment Score for Clinton: Tweets mentioning Hillary Clinton
tend to have a less positive or more negative sentiment compared to
Donald Trump.</li>
</ul>
</div>
<div id="implications" class="section level4">
<h4>Implications</h4>
<ul>
<li>Public Perception: Based on the data from tweets, Donald Trump
appears to have a more favorable sentiment among Twitter users compared
to Hillary Clinton.</li>
<li>Social Media Influence: The higher volume and possibly more positive
mentions of Trump on Twitter could be reflective of his stronger
presence or engagement on social media platforms.</li>
</ul>
<p><br></p>
</div>
</div>
<div id="analysis-based-on-journal-articles" class="section level3">
<h3>Analysis Based on Journal Articles</h3>
<p>The second bar chart compares the sentiment from journal articles for
both candidates.</p>
<div id="sentiment-distribution" class="section level4">
<h4>Sentiment Distribution</h4>
<ul>
<li>Donald Trump:
<ul>
<li>Negative Sentiment: Around 6000.</li>
<li>Positive Sentiment: Slightly higher than negative, around 6200.</li>
</ul></li>
<li>Hillary Clinton:
<ul>
<li>Negative Sentiment: Around 6800.</li>
<li>Positive Sentiment: Slightly lower than negative, around 6400.</li>
</ul></li>
</ul>
</div>
<div id="interpretation-1" class="section level4">
<h4>Interpretation</h4>
<ul>
<li>Balanced Sentiment for Trump: The sentiment towards Donald Trump in
journal articles is relatively balanced, with a slightly higher positive
sentiment.</li>
<li>Negative Sentiment for Clinton: Hillary Clinton has a higher
negative sentiment in journal articles compared to positive
sentiment.</li>
</ul>
</div>
<div id="implications-1" class="section level4">
<h4>Implications</h4>
<ul>
<li>Academic and Media Perception: Journal articles tend to have a more
balanced view of Trump, while Clinton receives more negative
sentiment.</li>
<li>Public Discourse: The sentiment in journal articles reflects the
complexities of each candidate’s public image, policies, and
controversies discussed in academic and media circles.</li>
</ul>
<p><br></p>
</div>
</div>
</div>
<div id="references" class="section level1">
<h1>References</h1>
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