K. Sunil Kumar,M. Ramana Kumar,Ammana Saketh reddy, Guguloth Santhosh, Dubbaka Saikiran 03 04(1) In contemporary organizations employee reviews shared on platforms such as Amazon represent a valuable source of insight into workplace dynamics however deriving meaningful information from this largely unstructured textual data remains a significant challenge Conventional approaches including manual inspection heuristicbased analysis and simple keyworddriven sentiment techniques are not only timeintensive and inconsistent but also inadequate for capturing the nuanced context and multiple dimensions embedded within employee feedback As a result organizations face difficulty in accurately assessing critical workforce factors such as worklife balance career progression and job security when dealing with large volumes of reviews These traditional methods lack scalability struggle with complex sentiment interpretation and often yield suboptimal accuracy underscoring the necessity for a more advanced and intelligent solution To overcome these limitations the proposed system employs a transformerbased Natural Language Processing NLP framework integrated within a realtime Flask web application utilizing SentenceBERT SBERT embeddings for deep semantic feature extraction alongside a Stochastic Gradient Descent SGD based multilabel classification model with a Hashing Vectorizer combined with Bernoulli Naive Bayes BNB serving as a comparative baseline The framework executes a comprehensive pipeline encompassing data preprocessing feature engineering model training prediction and evaluation enabling the simultaneous identification of multiple workplace experience indicators from a single review By leveraging contextual understanding and scalable automation this approach enhances prediction accuracy minimizes manual intervention and delivers actionable insights thereby supporting datadriven human resource analytics and informed organizational decisionmaking Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026