Qualitative data analysis involves exploring and interpreting non-quantitative data, such as textual information. This process encompasses collecting, structuring, and examining data to uncover themes and trends, address research questions, and uncover insights to enhance products, experiences, or the organization as a whole.

Using ChatGPT4 we will analyze 500 online reviews from 2023 for our pretend hotel, Crossroads Inn. The hotel is an inviting oasis directly across the street from the Mall of America. This prime location, combined with its carefully curated amenities, ensures every guest enjoys a convenient and luxurious stay.

Within the dataset, we have these columns:  CustomerID, Date, Review Score, Gender, Age, and then a birth year and generation, which are inferred from Age. We have the text of the reviews which are all under 200 words. As we know, ChatGPT sometimes hallucinates, that is, makes things up. I have also done this analysis in Python and Excel, so we have a source of truth.

The heart of qualitative data analysis lies in coding and thematic analysis. Coding involves labeling text segments with tags or codes representing their core idea or theme. This time-consuming step is crucial as it transforms raw text into categorized data, making it manageable and organized for deeper analysis. Following coding, thematic analysis takes center stage, where codes are reviewed, combined, and refined into significant themes that encapsulate the primary insights from the data. This phase is iterative, requiring multiple reviews to ensure that themes accurately represent the data's nuances.

Once the data has been thoroughly analyzed and insights gathered, the results are communicated to stakeholders and decision-makers to create positive organizational change.

Qualitative Data Analysis provides great meaningful information, but it is time-consuming. So, let’s have ChatGPT to make it faster for us.