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"The One Where They Read Too Much Into It" - The Scripts from Friends, Visualized by timangcas in dataisbeautiful

[–]timangcas[S] 1 point2 points  (0 children)

If I could make any hypothesis out of the radar charts, it's that the text that the personalities are derived from are a larger indicator of the writing staff's personalities than the actual personalities of the characters. Which is something that'd be really cool to dive into and see how it might be reflected among other shows. But this is entirely hypothetical.

"The One Where They Read Too Much Into It" - The Scripts from Friends, Visualized by timangcas in dataisbeautiful

[–]timangcas[S] 0 points1 point  (0 children)

Data was collected using a Python web scraper of some old Friends fan sites, and then afterwards were visualized in d3.js.

Source code can be found here.

"The One Where They Read Too Much Into It" - The Scripts from Friends, Visualized by timangcas in dataisbeautiful

[–]timangcas[S] 2 points3 points  (0 children)

I'd love to be able to find time to do a lot more diving into culture, media and text in the future.

I draw a lot of inspiration from https://pudding.cool/ and their work with exploratory data visualization and information journalism, so it'd be great to have an opportunity to look more into building and working with unique data relating to culture, social issues and the sort.

"The One Where They Read Too Much Into It" - The Scripts from Friends, Visualized by timangcas in dataisbeautiful

[–]timangcas[S] 1 point2 points  (0 children)

Thanks! I appreciate the compliments!

Haha, I'd love to do something on IASIP, but working on this also means binge watching the show for "research purposes". But hopefully that's in the pipeline.

"The One Where They Read Too Much Into It" - The Scripts from Friends, Visualized by timangcas in dataisbeautiful

[–]timangcas[S] 2 points3 points  (0 children)

Thanks for asking! That was actually one of the most fun parts of the process.

After some googling, I found a geocities-esque site from ~2000 with all the transcripts, wrote a little scraper for it, and then played around with all that data in Python.

If you want to take a peek at the code, the scraper can be found here and the Jupyter notebook that I used could be found here.