Article
Fine-Grained Opinion Mining from Employee Reviews for Organizational Decision Support
The rapid growth of digital platforms has led to the generation of large volumes of employee feedback data, making workforce satisfaction analysis an important area of study. Traditionally, organizations relied on manual surveys and basic statistical methods to evaluate employee satisfaction, which were often time-consuming and limited in capturing complex textual insights. With advancements in Natural Language Processing (NLP) and Machine Learning (ML), automated analysis has become feasible. However, existing approaches struggle with unstructured text, class imbalance, and multidimensional prediction tasks. The primary problem addressed in this study is the accurate prediction of workforce satisfaction factors such as work-life balance, skill development, salary and benefits, job security, career growth, and overall satisfaction from textual employee reviews. Traditional systems fail to process large-scale data efficiently and lack consistency in predictive performance. This creates the need for an intelligent framework capable of handling complex textual patterns and multi-label classification. To overcome these challenges, the proposed system integrates NLP preprocessing, transformer-based feature extraction using Google PaLM (Pathways Language Model – PaLM), and SMOTE (Synthetic Minority Over-sampling Technique). Multiple ML models including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), and Histogram-Based Gradient Boosting (HGB) are implemented and compared with the proposed Transformer-Guided Adaptive Model (TGAM). The results show that traditional models achieve moderate accuracy ranging from approximately 51% to 56%, while the proposed TGAM model achieves 100.00% accuracy across all target columns including work-life balance, skill development, salary and benefits, job security, career growth, and work satisfaction. This significant improvement highlights the effectiveness of the proposed approach in handling complex workforce data. The system also includes evaluation metrics and visualization techniques for better interpretability.
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