Hyperrhiz 14

BDP (Big Data Poetry)


Citation: , Jhave. “BDP (Big Data Poetry).” Hyperrhiz: New Media Cultures, no. 14, 2016. doi:10.20415/hyp/014.f02

Abstract: BDP (Big-Data Poetry) applies a combination of data visualization, language analytics, classification algorithms, entity recognition and part-of-speech replacement techniques to 3 corpuses: 10,557 poems from the Poetry Foundation, 57,000+ hip-hop rap songs from Ohhla.com, and over 7,000 pop lyrics. Based on these templates, a Python script generates thousands of poems per hour. Sometimes Jhave reads along with this writing machine, verbally stitching and improvising spoken poems.

Samples and Files

VIEW recreation of performance, ELO 2015, Bergen

VIEW code on GitHub (Hyperrhiz snapshot)

VIEW code on GitHub (Original BDP repo)

DOWNLOAD ELO 2015 code and data corpus (zip)

Requires Python package Anaconda 2.7. To run in command line:

>> cd code/poetryFoundation/ELO_July2015/
>> python ELO2015_PERF_Creeley-Aug4th.py