In this article, we delve into techniques for extracting valuable insights from customer feedback using Large Language Models (LLMs). By identifying themes, sentiment, and competitor comparisons from feedback, businesses can gain a competitive edge.
When considering bots, it’s often important for organizations to have the ability to “hand off” a customer from a bot to a human agent seamlessly. We implemented an unopinionated e-2-e solution called Handoff for bot authors to implement a variety of scenarios, using the Microsoft Bot Framework Node.js SDK.
An examination of whether a more sophisticated learner will always result in better performance in a text-based classifier, and the trade-off between accuracy and training time.
Investigations that provide insights on how to choose an N-gram feature that maximizes performance of a classifier.
In this code story, we explore how the topology of a deep neural network can affect the performance of a text-based classifier.
Utilising deep learning to detect emotions from short, informal English text.
