Uncovering Customer Issues through Topological Natural Language Analysis
Shu-Ting Pi Sidarth Srinivasan Yuying Zhu Michael Yang Qun Liu
E-commerce companies deal with a high volume of customer service requests
daily. While a simple annotation system is often used to summarize the topics
of customer contacts, thoroughly exploring each specific issue can be
challenging. This presents a critical concern, especially during an emerging
outbreak where companies must quickly identify and address specific issues. To
tackle this challenge, we propose a novel machine learning algorithm that
leverages natural language techniques and topological data analysis to monitor
emerging and trending customer issues. Our approach involves an end-to-end deep
learning framework that simultaneously tags the primary question sentence of
each customer's transcript and generates sentence embedding vectors. We then
whiten the embedding vectors and use them to construct an undirected graph.
From there, we define trending and emerging issues based on the topological
properties of each transcript. We have validated our results through various
methods and found that they are highly consistent with news sources.