Summary of Uncovering Customer Issues Through Topological Natural Language Analysis, by Shu-ting Pi et al.
Uncovering Customer Issues through Topological Natural Language Analysis
by Shu-Ting Pi, Sidarth Srinivasan, Yuying Zhu, Michael Yang, Qun Liu
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel machine learning algorithm is proposed to tackle the challenge of monitoring emerging and trending customer issues in e-commerce companies. The algorithm leverages natural language techniques and topological data analysis to tag primary question sentences in customer transcripts, generate sentence embeddings, and identify trends. The approach involves an end-to-end deep learning framework that simultaneously tags and embeds transcripts. Validation methods show high consistency with news sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary E-commerce companies get many customer service requests daily. It’s hard to thoroughly explore each issue because it takes time. This is especially important during a pandemic when companies need to quickly find and fix problems. To help, we created an algorithm that uses natural language processing and math to monitor emerging issues. The algorithm looks at customer transcripts, finds the main question, makes sense of the text, and connects related topics. We tested it and found it matches news sources. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Natural language processing
