Uncovering Customer Issues through Topological Natural Language Analysis
This addresses the challenge for e-commerce companies in quickly monitoring customer service issues, especially during outbreaks, though it appears incremental as it builds on existing NLP and TDA methods.
The paper tackled the problem of identifying emerging customer issues in e-commerce by proposing a machine learning algorithm that combines natural language processing and topological data analysis, and validated its results with high consistency against news sources.
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.