  Grounded theory (GT) is a qualitative research method for building theory
grounded in data. GT uses textual and numeric data and follows various stages
of coding or tagging data for sense-making, such as open coding and selective
coding. Machine Learning (ML) techniques, including natural language processing
(NLP), can assist the researchers in the coding process. Triangulation is the
process of combining various types of data. ML can facilitate deriving insights
from numerical data for corroborating findings from the textual interview
transcripts. We present an open-source python package (QRMine) that
encapsulates various ML and NLP libraries to support coding and triangulation
in GT. QRMine enables researchers to use these methods on their data with
minimal effort. Researchers can install QRMine from the python package index
(PyPI) and can contribute to its development. We believe that the concept of
computational triangulation will make GT relevant in the realm of big data.
