Transforming Open-Ended Survey Analysis: From Python to ChatGPT

In the realm of employee engagement surveys, open-ended questions provide rich, qualitative data that can reveal deep insights into employee sentiments and organizational culture. However, the challenge lies in effectively analyzing this data. In previous experiences, I relied on Python and NLP (Natural Language Processing) packages to make sense of this information. Recently, I decided to shift gears and experiment with ChatGPT. This article chronicles my journey from the early days of wrestling with code to discovering the potential of AI-driven text analysis.

The Python and NLP Journey

In previous experiences, my approach to analyzing open-ended survey responses involved leveraging Python and various NLP tools to process and extract insights from textual data. The process typically included topic modeling, sentiment analysis, and extensive data preprocessing. While these methods offered a high degree of control, they also came with challenges—particularly in handling large datasets, capturing nuanced language, and interpreting results in a meaningful way. The time investment was significant, and the complexity often required a deep understanding of both the data and the tools.

Why the Shift to ChatGPT

As AI technology advanced, I became increasingly curious about integrating more intuitive tools. Enter ChatGPT—a tool that promised to simplify the analysis process while delivering meaningful insights. My decision to try ChatGPT was driven by a desire to streamline the analysis, reduce manual effort, and explore new ways of extracting value from qualitative data.

Setting Up ChatGPT for Analysis

Getting started with ChatGPT was a straightforward process. I experimented with different prompts to guide the analysis, tailoring the instructions to focus on specific aspects common themes, like sentiment, or unique insights. The flexibility of ChatGPT allowed me to customize its approach to align with the goals of the survey analysis.

As I refined my approach, I decided to developed two separate GPTs to handle different aspects of the analysis:

1. Specific Topic Analysis GPT: This version was designed to analyze specific topics provided in the prompt, such as compensation or leadership. I was very satisfied with the performance of this GPT. It consistently delivered coherent and reliable insights, aligning well with previous sample data and providing similar answers across multiple runs.
2. Main Topic Identification GPT: The second GPT was tasked with identifying and quantifying the main topics from the survey responses. My satisfaction with this model was around 65%. The results varied, sometimes missing well-known top topics or producing inconsistent numbers of topics across different attempts. To improve accuracy, I found it necessary to compare the results with a sample, adjust the prompts, and re-run the analysis until the output met my expectations.

The Experience of Using ChatGPT

Process Overview:

I structured my prompts carefully, often starting with broad questions to gather general insights, then narrowing down to specific topics or sentiments. This iterative process allowed me to refine the analysis as I gained a better understanding of the data.

I started with very general instructions and gradually added more details. This included loading some sources like the item set, past responses, and examples of the desired output format. I found that the best results were obtained when the instructions were a balance between too few and too many details.

Importance of Subject Matter Expertise:

One of the key lessons from this experience is the importance of mastering the topic being analyzed. It’s not just about understanding the analytical tools but also about having deep knowledge of the subject matter. This expertise is crucial for discerning what is reasonable to find, distinguishing between noise and key insights, and identifying when findings make sense versus when they might be errors in the analysis. Without this understanding, it’s easy to misinterpret results or overlook critical insights.

Findings on Specific vs. General Analysis:

Specific Topic Analysis: When I asked ChatGPT to analyze specific topics, such as compensation or leadership, the tool performed exceptionally well. The responses were consistent and coherent, aligning closely with the results from a previous sample. When running the prompt multiple times, I received very similar answers each time, which reinforced the reliability of the tool in this context. This consistency underscored the importance of having a sample or point of reference to benchmark the analysis.

General Topic Identification and Quantification: On the other hand, when I tasked ChatGPT with finding and quantifying the main topics or calculating the frequency of these topics, the results were less satisfactory. The responses were often incoherent and inconsistent, varying widely across multiple attempts. For instance, a well-known top topic sometimes didn't appear in the list, and the number of topics identified fluctuated—some responses yielded ten topics, while others identified only three or four. This inconsistency highlighted the limitations of ChatGPT in handling broader, more exploratory tasks without clear guidance.

Frustrations: Misunderstandings:

There were instances where ChatGPT misinterpreted the context or generated responses that were too generic or off-topic. It took some trial and error to craft prompts that minimized these issues. Output Consistency: Ensuring consistent results was another challenge. While ChatGPT could provide varied insights, achieving uniformity across multiple analyses required constant tweaking.

Gains:

• Speed: One of the most significant advantages was the speed at which ChatGPT could analyze large volumes of text. What used to take hours or even days was now possible in a fraction of the time.
• Contextual Understanding: ChatGPT demonstrated an impressive ability to grasp context and nuance, especially when provided with well-crafted prompts. This led to richer insights compared to traditional NLP methods.
• Iterative Refinement: The ability to quickly iterate and adjust the analysis process was a game-changer. I could test different angles and immediately see the results, allowing for a more dynamic and responsive analysis.

Limitations:

• Prompt Dependence: The quality of the output was heavily dependent on the quality of the prompts. Crafting effective prompts required a balance of specificity and flexibility, which wasn’t always easy to achieve.
• Bias and Validation: Like any AI tool, ChatGPT is not free from biases. Ensuring that the generated insights were accurate and unbiased required careful oversight and, at times, manual intervention.

Conclusion

Reflecting on my experiences with both Python/NLP and ChatGPT, the differences are striking. Python and traditional NLP tools offered a high degree of control and customization but required significant effort and expertise. In contrast, ChatGPT provided a more user-friendly and efficient alternative, though with some trade-offs in consistency and control.

The findings from my experimentation showed that ChatGPT is highly effective when analyzing specific topics but less reliable when tasked with broader, exploratory analysis. This insight emphasizes the importance of having a reference point or sample data to guide the AI's analysis, ensuring that the results are both relevant and accurate. Additionally, mastering the topic being analyzed is crucial for interpreting the results accurately and making informed decisions.

Going forward, I see great potential in combining these tools—leveraging the speed and contextual understanding of ChatGPT alongside the precision and depth of traditional NLP methods. As AI technology continues to evolve, I’m excited to explore new possibilities for enhancing the analysis of open-ended survey data.

Closing Thoughts

While AI tools like ChatGPT are transforming the landscape of text analysis, it’s important to recognize their limitations. Human expertise remains crucial in guiding these tools and ensuring that the insights they generate are both accurate and actionable. I encourage others to experiment with these technologies, share their experiences, and contribute to the ongoing conversation about the future of AI in data analysis.