  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.
