Deep understanding of written user query

This project lies in the context of a collaboration between the HumanTech Institute and Kare Knowledgeware. Kare’s product is an automated knowledge retrieval conversational tool. The goal of the collaboration is to enhance the customer experience by adding empathy into the system. Empathy is possible by following two steps, first understanding the user intention, and second answering him accordingly.

The main objective is to develop a tool that will allow a user to extract useful information about his query: intent (informational vs emotional), sentiment (happy, upset). A user query enters the system, and this same query exits the system with annotations.

To achieve this goal, a system must be developed. This system has to:

1. Extract the intent from the query
2. Extract the emotion from the query

The current solution meets the initial objective. The intent classification model reaches 85% accuracy. The deep learning model for the emotion detection reaches 80% accuracy and the more simple model using hand-crafted features for the emotion detection reaches 60% accuracy, both on a problem consisting of 4 emotions so 4 classes: joy, anger, fear, sadness. Kare Knowledgeware can try these models on their data and see how the model performs. Hopefully, the models will perform well on their data too and they can work on answering user queries with more empathy.

General information
  • Date: 10.06.2020
  • Type: Semester project
  • Responsible: Jacky Casas