QRMine：一个用于Grounded Theory中三角剖分的Python包
QRMine: A python package for triangulation in Grounded Theory
摘要：
扎根理论（GT）是一种基于数据建立理论的定性研究方法。GT使用文本和数字数据，并遵循各种阶段的编码或标记数据的意义，如开放编码和选择性编码。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。三角测量是组合各种类型数据的过程。ML可以促进从数字数据中获得见解，以证实文本访谈记录的发现。我们提出了一个开源Python包（QRMine），它封装了各种ML和NLP库，以支持GT中的编码和三角测量。QRMine使研究人员能够以最小的努力在他们的数据上使用这些方法。研究人员可以从python包索引（PyPI）安装QRMine，并为其开发做出贡献。我们相信，计算三角测量的概念将使GT在大数据领域相关。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
