When you have a large collection of texts representing people’s opinions, such as product reviews, survey answers or social media, it is difficult to understand the key issues that come up in the data. Existing automated approaches are often limited to identifying recurring phrases or concepts and the overall sentiment toward them, but do not provide detailed or actionable insights.
In this workshop, we will show how to use Project Debater Early Access Program services for analyzing and deriving insights from answers to open-ended question. As an example, we will use Project Debater to analyze a collection of comments, from a community survey conducted in the city of Austin, and summarize it as a small set of key points.
This is the #4 session of the series:
Session 1 (6/16): Project Debater: From an AI debating system to business applications
Session 2 (6/30): NLP Workshop - Summarize and Find Actionable Insights from Textual Survey Data
Session 3 (7/7): NLP Workshop - Summarize and Find Actionable Insights from Textual Survey Data
Yoav Kantor(IBM AI)
research scientist at IBM - Haifa Research Lab. In the last six years he has been part of Project Debater research team. The current aim of this team research is the development of Computational Argumentation technologies. They are developing and exploiting a combination of Machine Learning and NLP techniques in this context. Yoav main focus in the past few years was the development of a new text summarization method called Key Point Analysis.