How does text classification work?


1. Text embeddings are generated for the open-ended responses using an AI embeddings model.

2. A k-means cluster analysis is conducted on the responses.

3. The k-means model is used to obtain a random sample of open-ended responses from each cluster.

4. A category name is generated for each sample using a generative AI model.

5. The k-means categories are replaced with the new category names and added to the dataset.

It’s also important┬áto emphasize that you should review and validate the category names (and associations with open-ended responses) since an AI model cannot be held responsible for its work.