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.
First, Sign in and Go to Kimola Cognitive home page.
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.
What is a dataset for training?
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:
A Quick Guide to Create The Best Dataset for Training:
The text content must be about a topic, a brand or anything that can be classified
The data should be classified with a minimum of two and a maximum of eight labels
Each label should have a minimum of 500 rows of text.
Content should be distributed equally on each label.