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https://github.com/nolanbconaway/pitchfork-data

Analyses on over 18,000 pitchfork reviews.
https://github.com/nolanbconaway/pitchfork-data

data-science ipynb jupyter music pitchfork

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Analyses on over 18,000 pitchfork reviews.

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# Pitchfork Data

I scraped over 18,000 [Pitchfork](http://pitchfork.com/) reviews, going back to January 1999. I'll be analyzing the data to satisfy a few of my own curiosities.

## Notebooks:

- [[Link](notebooks/statistical-heaping.ipynb)]. Some evidence of statistical heaping in the review scores.
- [[Link](notebooks/review-score-exploration.ipynb)]. An exploration of review scores.
- [[Link](notebooks/reviewer-development.ipynb)]. Do writers get tougher with experience? (answer: *no*.)
- [[Link](notebooks/best-new-music-iid.ipynb)]. Is "Best New Music" sampled [IID](https://en.wikipedia.org/wiki/Independent_and_identically_distributed_random_variables)? (answer: *yes*.)
- [[Link](notebooks/artist-development.ipynb)]. Is the first album the best? (answer: *no, but the last album is the worst*.)
- [[Link](notebooks/author-autocorrelation.ipynb)]. Are reviews autocorrelated? (answer: *yes*.)

## Some other things i want to know:

- Are best new music reviews longer? shorter? Same Q for very low ratings.
- What is avg. amount of time between original and reissue date?
- Can candidates for re-release be predicted based on first-round review content?
- Can you predict when an album should be re-released based on current data?