IBM Watson turns survey responses into data on five emotions, entities, relations, and more. Click here to see the full dataset.

ChatGPT is by far the most powerful tools out there. I took all of the NLU datasets and used them to create two reports, one for semantic analysis and the other topic modeling. The semantic analysis report finds the top five themes found in the words and concepts in the data. Topic modeling reveals the top unique topics found in the words of people's responses. Together, they help us see the feelings underlying the opinion in their quantitative responses.

Above are the first few lines of an Emotions Report, created by our custom LLM. This is one of six reports I generate as part of a regular analysis. These in-depth reports provide usable insights for understanding the impact and influence of emotion in the responses. We can use the data to create visualizations, data picture stories that provide insights visually. Click here for the full report.

These are the most popular and relevant terms found in the sentence subjects of people's responses. In this case, the responses are filtered by the respondents' income level. Similarities across income levels are as fascinating as the range and intensity of subjects. For example, "a living wage" is least relevant to "not reported", then poor, then the working poor, middle class, upper class and the working class (not surprisingly). It's also the only subject shared by almost all income levels. Another thing easy to see that topics important to the working class are really, really important to them. Click here for the full portfolio.

I used the semantic analysis to create personas and topic analysis for persona variants. By personalizing the top five themes and various subtopics, it becomes easier to contextualize them. For example, you and I can frame the findings as personal to Rachel or people like Rachel. Or we can think about a punch list of demographic and psychographic values. It's easier to write and advocate for a person.

First, an observation. There is no way a human is going to glean this by reading 321 responses. The machine analyzes and retains at scale. Second, is knowing that the primary creative document is supported by a firm foundation of data; presented in a practical way. Click here for the full creative brief.