I HEAVILY use textiq. The key is building up your topic JSON file (I bet you could chatgpt to get started to cater towards your specific business needs). For example I work in the health care field. I have a topic of “Facility” where it tags for:

An example of what I run to tag “communication”

One more piece of advice - if you have several open text boxes I have found it helpful to join them all together in a combine field in field editor and also analyze that way.

For example if you ask an NPS question and then have 2 follow up options (if promoter “what is the primary reason for your rating” and passive/detractor “what can we do to improve”). What I was struggling with prior to combining fields is my dashboard end users (I had textiq widets/filters) who would look at a single question and it would look like the open text comments that mentioned pay were all very positive (for simple math we will say that 100 people responded mentioned pay and 90% of it was positive) this was because it was pulling from “what is the primary reason behind your rating” question. But if they looked at the pay tag/topic under the “what can we do to improve” question it was mostly/all negative (we’ll say that only 20 responded and 18 were negative) - BUT when we combined the questions we then can see a true percentage. We had 120 total open texts that mentioned pay, 90 were positive, 18 negative - that gives me a very different picture then when analyzing each question individually if I’m trying to see an overall sentiment.

I do still run each question individually as well, but when building reports/dashboard I lean towards using combined open text fields with TextiQ to display the data in a more wholistic and consumable way.