Analyzing reviews, feedback, and conversations of consumers is a never-ending process for marketing pros, founders, researchers, and any dataholic. If you’re working for an enterprise-level brand and constantly tracking conversations, ready-made ML models at Kimola Gallery might not solve your problem, so it’s best to create a custom ML model. With a custom ML model, you can decide the labels that your content will be classified and keep classifying the upcoming data.
So let’s dive in to see how you can create a custom ML model.
If you’re already a member of Kimola Cognitive, the first thing you should do is to sign in and go to your home page of Kimola Cognitive. On your home page, click the plus icon as shown in this screenshot:
Now, the onboarding screen should appear. This screen is a walkthrough to create your new ML model.
As also stated in the onboarding screen, you will need to upload a dataset to train your ML model.
A custom ML model is mostly created to analyze thousands of conversations with defined labels. Your training set should contain texts, as Kimola Cognitive only analyzes text data. Here is an example of a dataset to upload for training: