What is a Model?
3 mins read - Updated on Oct 23, 2025In Kimola, a Model is an artificial intelligence (AI) system trained to understand and interpret text data. Each model has a specific purpose: it analyzes written content (such as social media posts, product reviews, or survey answers) and transforms it into structured insights.
Sign in to your Kimola account and go to the Models section on the left panel.
You can think of a model as a “specialist” — each one is trained to focus on a certain type of task.
Some models detect emotions in feedback, while others extract names or topics from a sentence.
There are also models built for specific domains or goals:
- The News Classifier, for instance, automatically groups online articles into categories like politics, economy, technology, or entertainment.
- The Automotive Feedback Classifier helps brands understand customer opinions by identifying whether a comment is about design, comfort, fuel efficiency, or after-sales service.
- The Entity Extractor can pick out mentions of people, organizations, and locations from large volumes of text, helping researchers map out what consumers talk about most.
Each of these models has its own expertise — together, they turn raw text into structured, meaningful insights that support better decisions.
Classifiers
Classifiers are models that categorize text into predefined labels.
They answer questions like:
- “Is this review positive or negative?”
- “Which topic does this post belong to — product quality, delivery, or customer service?”
Each classifier is trained on thousands of examples so it can automatically assign new pieces of text to the correct category.
The Banking Feedback Classifier can analyze customer comments about a bank and group them into themes such as mobile banking, customer support, or interest rates — helping you instantly see where satisfaction or frustration is strongest.
Extractors
Extractors are models designed to find and pull specific pieces of information from text.
Unlike classifiers, which decide what a message is about, extractors look inside the message and pick out the meaningful details hidden within it.
Entity Extractor in Kimola can read a sentence and automatically identify:
- Organizations: e.g., Apple, Google, Netflix
- Languages: e.g., Spanish, Mandarin, English
- Colors: e.g., pink, blue, green
- Locations: e.g., New York, Paris, Europe
- People e.g., Elon Musk, Taylor Swift
This helps transform long, unstructured text into structured data that can be analyzed easily.
Extractors are especially useful when you want to build datasets or spot trends — such as the most mentioned brands, products, or places in customer feedback.
If hundreds of social media posts mention “delivery delay” or “customer service,” an extractor helps you identify which brands or locations those conversations are associated with.
In Short
Models transform unstructured text into clear, actionable insights.
🔹 Classifiers help you understand what the feedback is about — for example, whether it’s about product quality, delivery experience, or customer service, and whether the tone is positive, neutral, or negative.
🔹 Extractors reveal what’s inside the message — such as the brands, products, or locations being mentioned.
When used together, they give you a complete picture of your audience’s opinions, emotions, and experiences — all at scale and without manual effort.
