Custom Classifier 2.0

Custom Classifier 2.0 is a revolutionary way to classify any piece of text into custom categories. Typically, text classifiers like sentiment analysis or email classification can be classified into predefined set of categories on which they were trained.

However, in today’s business environment, requirements can change very often and therefore, any text classification task would need to be updated by adding more categories over time. Often, this means an expensive and time consuming job of manually labeling data and training a text classification algorithm on the newly created dataset.

With custom classifier, you can eliminate all the overheads involved with training a custom text classification engine and focus on your business use-case.

try our custom classifier 2.0 demo

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In the demo below, provide an input text and some categories (labels) in which you want to classify the text.

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Why our Custom Classifier 2.0 API ?

Accurate

Accurately classifies unstructured textual data.

Real Time

Uses cutting edge technology to provide results in real time.

Customizable

Can be easily trained on custom datasets to enhance model performance.

How Our Custom Classifier 2.0 API Works?

Custom classifier 2.0 is based on the zero shot learning technique. Thus, it can even generalize to new unseen categories when classifying a piece of text. It can do this because it is trained on millions of lines of text taken from different news websites and forums so like a human, it can understand the meaning of a piece of text and could relate it to the appropriate categories when presented.

It also uses multiple neural network algorithms that use a training methodology to get near accurate results on a variety of datasets. One of the best methodologies is to use a Long Short-Term Memory (LSTM) model for the task of learning relationships. Through constant feedback connections, the LSTM algorithm used in Custom Classifier 2.0 helps in establishing the concept of "belongingness" between sentences and classes in the model. This knowledge is now useful for unseen classes or even unseen datasets.

During our beta test, our users have tried this solution on varied use-cases. The biggest use-case that came out was classification of open-ended survey responses and online reviews. For both the use-cases, you may define different categories for different types of survey questions or product being reviewed. For example, a seller on Amazon selling mobile phones may want to classify the reviews into categories like battery life, camera, build quality, processor, screen while another seller selling clothes may classify reviews into categories like fitting, quality, fabric, etc.

use cases

Analyze Verbatim Comments

Verbatim comments may pose a challenge at first but analyzing them can be made easy and simple with the help of Deep Learning. We have launched custom classifier 2.0 to allow anyone to analyze verbatim comments quickly and accurately without writing a single line of code. The algorithm deciphers the output of the data set by mining key phrases that are tracked to particular labels. Read more...

Customer Survey Analysis

There is a need to distinguish between positive and negative feedback while performing survey analysis. Negative feedback is a goldmine of potential product enhancements, and positive feedback tells you where you are adding the most value to your customers. Using the add-on makes it super easy to use the AI and find out the underlying sentiment behind thousands of text pieces in one go. Read more...

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