Dig deeper or test it for yourself

Information is key, and the Peltarion team is continuously producing educational and inspirational material to help you along your AI journey. And below, we have listed some useful content to help you get started with your own Text classification projects on the platform.

02/ Blog - Building a whiskey classifier with Bubble and Peltarion

It has been nearly 20 years since Whiskey Classified was first published, a book that revolutionized our appreciation of whisky. David Wishart guided us through the process of identifying the aroma and taste of whiskey using simple day-to-day adjectives, therefore, opening the door to understanding whiskey to a much bigger population. The global whiskey market size was valued at USD 57.96 billion in 2018 and is projected to register a CAGR of 6.4% over the forecast period.

03/ Tutorial - Classify text in any language

If you have done our previous tutorials, you might be aware that AI has made big strides in being able to automatically identify relationships and context in text data, and through that extract critical information locked in it. However, as is the nature of many AI advances, many of these capabilities were only available for text written in English. But not anymore! In this tutorial we will show you how you can use the Peltarion Platform and its Multilingual BERT snippet to create a model that is able to work with multiple languages simultaneously!

Multilingual book corpus

04/ Tutorial - Book genre classification

Bookstores rarely split them apart, but we at Peltarion argue that fantasy and science fiction clearly are different things. To make the point, we decided to create an AI model that classifies the genre of a book solely on its summary. In this tutorial, we’ll show you how to use the Peltarion Platform to build a model on your own and correct all major bookstores in your country!

05/ Tutorial - Classify customer complaints

No one likes to think about customer complaints. You want to spend all your time trying to build a product so good that you won’t receive any. But when they eventually do trickle in, it’s good to know right away what the customer complains about so you can get to the bottom of it. In this tutorial, you will build an AI classifier and a PowerApp to go with it.

06/ Tutorial - Movie review feelings

In this tutorial, you will solve a text classification problem using BERT (Bidirectional Encoder Representations from Transformers). The input is an IMDB dataset consisting of movie reviews, tagged with either positive or negative sentiment – i.e., how a user or customer feels about the movie. Text classification aims to assign text, e.g., tweets, messages, or reviews, to one or multiple categories. Such categories can be the author’s mood: is a review positive or negative?

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