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. But 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 . 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 and to shed light on the user insights, according to the paper . The system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling. It provides a system that mapped the top Words to the Self-Help issues by using web-Crawling. This work is a powerful tool

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Keywords : learning - user - approach - insights - support -

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