In this session you’ll learn how Tailwind Traders took their support ticket text and audio files, convert and extract insight metadata from each ticket using Azure Cognitive Services Text Analytics and Speech-to-Text.

They then aggregated their findings to inform their product backlog and implement improvements.

Tailwind Traders have a great website and application for customers and partners. However, they are seeing an increased amount of support tickets regarding usage of these offerings. They want to store, analyze and extract insights from their text and audio data to make better product backlog decisions and reduce their support tickets. 

     

Learn about the recently launched Opinion Mining and Async offerings of Text Analytics.

From the video description:

In the first half, we will discuss how Opinion Mining (an extension of Sentiment Analysis) helps explore customers’ perception of aspects/opinions, such as specific attributes of products or services, in text. In the second half, we will learn about the new Async capabilities of Text Analytics, which will allow bundling various skills of Text Analytics and also allows large amount of text of up to 125K characters to be sent to Text Analytics via /analyze endpoint.

Text Analytics for health is a preview feature of Text Analytics which enables developers to process and extract insights from unstructured clinical and biomedical text.

Through a single API call, using NLP techniques such as named entity recognition, entity linking, relation extraction and entity negation, Text Analytics can extract critical and relevant medical information without the need for time-intensive, manual development of custom models.