Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support

• URL: http://arxiv.org/abs/2007.09303v3
• Date: Wed, 22 Jul 2020 00:20:23 GMT
• Title: Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support
• Authors: Xin Deng, Ross Smith, Genevieve Quintin
• Abstract summary: We propose and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling. This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model. Our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling.
• Score: 9.66491980663996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the evolution of the cloud and customer centric culture, we inherently accumulate huge repositories of textual reviews, feedback, and support data.This has driven enterprises to seek and research engagement patterns, user network analysis, topic detections, etc.However, huge manual work is still necessary to mine data to be able to mine actionable outcomes. In this paper, we proposed and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling to have a better understanding of the user voice.This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning, aiming at automating the process of better identifying the main topics or areas as well as the sub-topics from the textual feedback and support.There are three major break-through: 1. As the advancement of deep learning technology, there have been tremendous innovations in the NLP field, yet the traditional topic modeling as one of the NLP applications lag behind the tide of deep learning. In the methodology and technical perspective, we adopt transfer learning to fine-tune a BERT-based multiclassification system to categorize the main topics and then utilize the novel PSHTI model to infer the sub-topics under the predicted main topics. 2. The traditional unsupervised learning-based topic models or clustering methods suffer from the difficulty of automatically generating a meaningful topic label, but our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling. 3. This work provides a prominent showcase by leveraging the state-of-the-art methodology in the real production to help shed light to discover user insights and drive business investment priorities.

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