Sentiment Analysis of 5 Popular Romantic Comedies: Text Analysis in R
<em><strong>Updated</strong>: December 30, 2022.</em>
With Valentine’s Day coming up, I was thinking about a fun analysis that I could convert into a blog post - an R sentiment analysis based on the top 5 romantic comedies.
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Inspired by a beautiful visualization <a href="https://informationisbeautiful.net/visualizations/based-on-a-true-true-story/" target="_blank" rel="noopener noreferrer">“Based on a True True story?”</a>, I decided to do something similar: a sentiment analysis in the most popular romantic comedies.
After searching for <em>romantic comedies</em>, Google suggests a list of movies. Top 5 are: <em><a href="http://www.imdb.com/title/tt0098635/?ref_=nv_sr_3" target="_blank" rel="noopener noreferrer">When Harry Met Sally</a></em>, <em><a href="http://www.imdb.com/title/tt0314331/" target="_blank" rel="noopener noreferrer">Love Actually</a></em>, <em><a href="http://www.imdb.com/title/tt0100405/?ref_=nv_sr_1" target="_blank" rel="noopener noreferrer">Pretty Woman</a></em>, <em><a href="http://www.imdb.com/title/tt0125439/?ref_=nv_sr_1" target="_blank" rel="noopener noreferrer">Notting Hill</a></em>, and <em><a href="http://www.imdb.com/title/tt0108160/?ref_=nv_sr_2" target="_blank" rel="noopener noreferrer">Sleepless in Seattle</a></em>.
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<h2 id="how-to-do-text-analysis-in-r">How to do Text Analysis in R</h2>
We can use the <code class="highlighter-rouge">subtools</code> package to analyze the movies’ sentiment in R by loading the movie subtitles into R and then use <code class="highlighter-rouge">tidytext</code> to work with the text data.
<pre><code class="language-r">library(subtools)
library(tidytext)
library(dplyr)
library(plotly)
library(purrr)
library(lubridate)
library(methods)
library(plyr)</code></pre>
<h3 id="working-with-movie-data">Working with movie data</h3>
I downloaded the srt subtitles for 5 comedies from <a href="https://www.opensubtitles.org/en/?" target="_blank" rel="noopener noreferrer">Open Subtitles</a> before the analysis.
Now let’s load them into R and have a sneak peak of what the data looks like.
<pre><code class="language-r">romantic_comedies_titles <- c(
"Love Actually", "Notting Hill", "Pretty Woman",
"Sleepless in Seattle", "When Harry Met Sally"
)
subtitles_path <- "../assets/data/valentines/"
<br>romantic_comedies <- romantic_comedies_titles %>% map(function(title){
title_no_space <- gsub(" ", "_", tolower(title))
title_file_name <- paste0(subtitles_path, title_no_space, ".srt") subtools::read_subtitles(title_file_name) %>%
mutate(movie_title = title)
})
<br>head(romantic_comedies[[1]])</code></pre>
<img class="size-full wp-image-17235" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b0235a4a4af475c4356630_1-7.webp" alt="Image 1 - Subtitle data" width="1264" height="270" /> Image 1 - Subtitle data
<h3 id="subtitles-preprocessing">Subtitles preprocessing</h3>
The next step is <strong>tokenization</strong>, chopping up the subtitles into single words. At this stage I also perform a minor cleaning task, which is removing <strong>stop words</strong> and adding information about the line and its duration.
<pre><code class="language-r">tokenize_clean_subtitles <- function(subtitles, stop_words) { subtitles %>%
unnest_tokens(word, Text_content) %>%
anti_join(stop_words, by = "word") %>%
left_join(subtitles %>% select(ID, Text_content), by = "ID") %>%
mutate(
line = paste(Timecode_in, Timecode_out),
duration = as.numeric(hms(Timecode_out) - hms(Timecode_in)))
}
<br>data("stop_words")
<br>head(stop_words)</code></pre>
<div class="mceTemp"></div>
<img class="size-full wp-image-17237" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b0207bce6734eff8380a6b_2-5.webp" alt="Image 2 - Stopwords" width="263" height="266" /> Image 2 - Stopwords
<pre><code class="language-r">tokenize_romantic_comedies <- romantic_comedies %>%
map(~tokenize_clean_subtitles(., stop_words))</code></pre>
After tokenizing the data I need to classify the <strong>word sentiment</strong>. In this analysis I simply want to know if the word has positive or negative sentiment. <code class="highlighter-rouge">Tidytext</code> package comes with <a href="https://www.tidytextmining.com/sentiment.html#comparing-the-three-sentiment-dictionaries" target="_blank" rel="noopener noreferrer">3 lexicons</a>. The <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html" target="_blank" rel="noopener noreferrer">bing</a> lexicon categorizes words as positive or negative. I use <em>bing</em> lexicon to assign the extracted words into desired classes.
<pre><code class="language-r">bing <- tidytext::get_sentiments("bing")
<br>assign_sentiment <- function(tokenize_subtitles, bing) { tokenize_subtitles %>%
left_join(bing, by = "word") %>%
mutate(sentiment = ifelse(is.na(sentiment), "neutral", sentiment)) %>%
mutate(score = ifelse(sentiment == "positive", 1, ifelse(sentiment == "negative", -1, 0)))
}
<br>tokenize_romantic_comedies_with_sentiment <- tokenize_romantic_comedies %>%
map(~ assign_sentiment(., bing))</code></pre>
Since I am interested in deciding the sentiment of the movie line, I need to aggregate the scores on the line level. I create a simple rule: if the overall sentiment score is <code class="highlighter-rouge">>= 1</code> we classify the line as positive, negative when <code class="highlighter-rouge"><= -1</code> and neutral in the other cases.
<pre><code class="language-r">summarized_movie_sentiment <- function(tokenize_subtitles_with_sentiment) { tokenize_subtitles_with_sentiment %>%
group_by(line) %>%
summarise(
sentiment_per_minute = sum(score),
sentiment_per_minute = ifelse(sentiment_per_minute >= 1, 1, ifelse(sentiment_per_minute <= -1, -1, 0)), line_duration = max(duration), line_text = dplyr::first(Text_content), movie_title = dplyr::first(movie_title) ) %>%
ungroup() %>%
mutate(perc_line_duration = line_duration / sum(line_duration))
}
<br>summarized_sentiment_romantic_comedies <- tokenize_romantic_comedies_with_sentiment %>%
map(~ summarized_movie_sentiment(.))
<br>summarized_sentiment_romantic_comedies</code></pre>
<img class="size-full wp-image-17239" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b27075e7bf5d6c2d302459_3-3.webp" alt="Image 3 - Summarised sentiment" width="1542" height="466" /> Image 3 - Summarised sentiment
<h3 id="crème-de-la-crème---data-viz">Crème de la crème: data visualization</h3>
After I am done with data preparation and munging, the fun begins and I get to visualize the data. In order to achieve a similar look as the authors of “Based on a True True Story?”, I use stack horizontal bar charts in <code class="highlighter-rouge">plotly</code>. The bar length represents the movie duration in minutes.
<em>Hint:</em> Hover on the chart to see the actual line and time.
<img class="size-full wp-image-17233" src="https://webflow-prod-assets.s3.amazonaws.com/6525256482c9e9a06c7a9d3c%2F65b02117fd01060a37e07506_1-6.webp" alt="Image 4 - Sentiment analysis visualization" width="780" height="186" /> Image 4 - Sentiment analysis visualization
<pre><code class="language-r">sentiment_freq <- round(
ldply(summarized_sentiment_romantic_comedies, data.frame) %>%
group_by(factor(sentiment_per_minute)) %>%
summarize(duration = sum(perc_line_duration)) %>% .$duration * 100, 0
)
<br>plot_title <- paste(
"<b>", summarized_sentiment_romantic_comedies$movie_title[1], "</b>",
'<span style="color: #FA0771">Positive', paste0(sentiment_freq[3], "%</span>"),
'<span style="color: #01A8F1">Negative', paste0(sentiment_freq[1], "%</span>")
)
<br>plot_ly(ldply(summarized_sentiment_romantic_comedies, data.frame),
y = ~movie_title, x = ~perc_line_duration,
type = "bar", orientation = "h", color = ~sentiment_per_minute,
text = ~ paste("Time:", line, "<br>", "Line:", line_text),
hoverinfo = "text", colors = c("#01A8F1", "#f7f7f7", "#FA0771"),
width = 800, height = 200
) %>%
layout(
xaxis = list(
title = "", showgrid = FALSE, showline = FALSE,
showticklabels = FALSE, zeroline = FALSE,
domain = c(0, 1)
),
yaxis = list(title = "", showticklabels = FALSE),
barmode = "stack",
title = ~plot_title
) %>%
hide_colorbar()</code></pre>
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<h2 id="next-steps">Next steps</h2>
Recently, I learned about <code class="highlighter-rouge">sentimentR</code> package that lets you analyze the sentiment on the sentence level. This would be interesting to conduct the analysis that way and see what sentiment scores would be received.
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