Background:

Qualitative methods analyze contextualized, unstructured data. These methods are time and cost intensive, often resulting in small sample sizes and yielding findings that are complicated to replicate. Integrating natural language processing (NLP) into a qualitative project can increase efficiency through time and cost savings; increase sample sizes; and allow for validation through replication. This study compared the findings, costs, and time spent between a traditional qualitative method (Investigator only) to a method pairing a qualitative investigator with an NLP function (Investigator +NLP).

Methods:

Using secondary data from a previously published study, the investigators designed an NLP process in Python to yield a corpus, keywords, keyword influence, and the primary topics. A qualitative researcher reviewed and interpreted the output. These findings were compared to the previous study results.

Results:

Using comparative review, our results closely matched the original findings. The NLP + Investigator method reduced the project time by a minimum of 120 hours and costs by $1,500.

Discussion:

Qualitative research can evolve by incorporating NLP methods. These methods can increase sample size, reduce project time, and significantly reduce costs. The results of an integrated NLP process create a corpus and code which can be reviewed and verified, thus allowing a replicable, qualitative study. New data can be added over time and analyzed using the same interpretation and identification. Off the shelf qualitative software may be easier to use, but it can be expensive and may not offer a tailored approach or easily interpretable outcomes which further benefits researchers.

中文翻译：

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将自然语言处理融入质性研究的方法

背景：

定性方法分析上下文相关的非结构化数据。这些方法是时间和成本密集型的​​，通常导致样本量小并且产生难以复制的发现。将自然语言处理（NLP）集成到定性项目中可以通过节省时间和成本来提高效率；增加样本量；并允许通过复制进行验证。这项研究比较了传统定性方法（仅限研究人员）与定性研究人员与NLP功能配对的方法（研究人员+ NLP）之间的发现，成本和花费的时间。

方法：

研究人员使用先前发表的研究中的辅助数据，在Python中设计了一个NLP流程，以生成语料库，关键字，关键字影响力和主要主题。定性研究人员审查并解释了输出结果。将这些发现与以前的研究结果进行了比较。

结果：

通过比较审查，我们的结果与原始结果非常吻合。NLP +研究人员方法将项目时间至少缩短了120小时，成本降低了1,500美元。

讨论：

通过结合NLP方法，定性研究可以发展。这些方法可以增加样本量，减少项目时间并显着降低成本。NLP集成过程的结果将创建一个语料库和代码，可以对其进行检查和验证，从而可以进行可复制的定性研究。随着时间的推移，可以使用相同的解释和标识来添加和分析新数据。定性的定性软件可能更易于使用，但价格昂贵，可能无法提供量身定制的方法或易于解释的结果，从而进一步使研究人员受益。