Context Pitchfork is an online music magazine that was launched in 1995. Originally focused on newly released independent music, Pitchfork has grown to become one of the biggest, and arguably, relevant voices in music journalism covering all types of popular music. The site primarily features critical reviews of new and reissued albums. When written well these reviews can provide additional insight into the artist such as the artists influences, historical context surrounding the music as well as how the album fits into broader cultural trends. The review authors will often reference other artists or albums indicating the reference is influential or culturally significant. Full analysis of the dataset can be found [here][1] including interactive visualizations. Content The Pitchfork review dataset [here][2] which captures the Pitchfork reviews from 1999 to 2017 as well as additional information around the reviews such as the author of the review, genre classification of the album being reviewed, label the album was released on, year the review was published, etc. I used the Google Natural Language (NL) API to extract entities from the reviews where the text is inspected for proper nouns such as names of individuals or titles of specific pieces of art, as well as common nouns such as music or violin. Acknowledgements Thank you to user Nolan Conway for providing the original review dataset, the Pitchfork authors and of course the musicians of the releases! Inspiration This project will measure the artists and albums Pitchfork reviewers reference the most to determine works of cultural significance. [1]: http://jk.zone/pitchfork-between-the-lines/ [2]: https://www.kaggle.com/nolanbconaway/pitchfork-data