论文标题
QRMine：底层理论中三角剖分的Python软件包
QRMine: A python package for triangulation in Grounded Theory
论文作者
论文摘要
接地理论（GT）是一种基于数据基础的构建理论的定性研究方法。 GT使用文本和数字数据，并遵循编码或标记数据的各个阶段进行意识制作，例如打开的编码和选择性编码。机器学习（ML）技术，包括自然语言处理（NLP），可以帮助研究人员进行编码过程。三角剖分是结合各种数据的过程。 ML可以促进从数值数据中获得见解，以证实文本访谈成绩单的发现。我们提出了一个开源Python软件包（QRMine），该软件包封装了各种ML和NLP库，以支持GT中的编码和三角剖分。 QRMine使研究人员能够以最小的努力在数据上使用这些方法。研究人员可以从Python软件包指数（PYPI）中安装QRMine，并可以为其开发做出贡献。我们认为，计算三角剖分的概念将使GT在大数据领域中相关。
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.
