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The other day, YouTube recommended a trailer for the upcoming Superman movie. Naturally, instead of just watching it and moving on like a normal person, my brain went:
“What are the most common plots in today’s top movies?”
Yeah…I do not understand how my brain works either.
It struck me how many recent blockbusters seem to follow similar themes: superheroes (still going strong, somehow), explosive action flicks, and horror films (which I respectfully avoid because I enjoy sleeping at night). So rather than doing what a reasonable human might do, and just do a simple Google search, I built an entire Python scraper (which, to be honest, was done only because I really wanted to have a data-mining project, but let’s just pretend like it was an attempt at being bold, quirky, and different).
My script pulls the current Top 25 movies from IMDb (technically Top 50, but I was only able to fetch the Top 25, more details in the repo), grabs each one’s plot summary, and then generates a word cloud from all that text. The result? A visual answer to the question: “What are audiences being served most often right now, plot-wise?” And as a bonus, I made it work for specific genres too, so you can visualize what “sells” in action, comedy, or even horror (if you're braver than me).
What I Learned:
This wasn’t just a fun little project that I worked on, my main goal was to learn a lot of new stuff along the way:
Has This Been Done Before?
Absolutely.
But was it worth building myself?
100%.
Because I didn’t just end up with a cool visual, I walked away with skills that go way beyond this one project. And that’s the power of learning by doing.
As a bonus, it also fetches the most common genres:
“What are the most common plots in today’s top movies?”
Yeah…I do not understand how my brain works either.
It struck me how many recent blockbusters seem to follow similar themes: superheroes (still going strong, somehow), explosive action flicks, and horror films (which I respectfully avoid because I enjoy sleeping at night). So rather than doing what a reasonable human might do, and just do a simple Google search, I built an entire Python scraper (which, to be honest, was done only because I really wanted to have a data-mining project, but let’s just pretend like it was an attempt at being bold, quirky, and different).
My script pulls the current Top 25 movies from IMDb (technically Top 50, but I was only able to fetch the Top 25, more details in the repo), grabs each one’s plot summary, and then generates a word cloud from all that text. The result? A visual answer to the question: “What are audiences being served most often right now, plot-wise?” And as a bonus, I made it work for specific genres too, so you can visualize what “sells” in action, comedy, or even horror (if you're braver than me).
What I Learned:
This wasn’t just a fun little project that I worked on, my main goal was to learn a lot of new stuff along the way:
- Data cleaning & preprocessing using tools like NLTK (stopwords, tokenization)
- Web scraping with requests and BeautifulSoup
- Visualizing data using WordCloud and matplotlib
- Handling website structure quirks and anti-bot headers
- Using time.sleep() to avoid hammering IMDb’s servers (being nice to websites)
Has This Been Done Before?
Absolutely.
But was it worth building myself?
100%.
Because I didn’t just end up with a cool visual, I walked away with skills that go way beyond this one project. And that’s the power of learning by doing.
As a bonus, it also fetches the most common genres: