Hybrid Machine Learning and NLP Approaches for Sentiment Productivity Analysis of Employees

The active involvement and well-being of employees have an immense effect on organizational productivity. However, traditional review procedures for employee engagement often rely on manual analysis of qualitative feedback, which makes them ineffective and biased. In order to improve the precision and effectiveness of employee assessments, this study suggests an automated framework that combines Machine Learning (ML) and Natural Language Processing (NLP). The system associates the structured performance and the attendance data with textual feedback submitted by employees to show a comprehensive analysis of employee happiness. Prior to TF-IDF vectorisation, text data is pre-processed using common NLP techniques like tokenisation, stemming, and stop-word removal. To sort out the feedback as positive, neutral, or negative, we have used machine learning models such as Random Forest, Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. Logistic Regression outperformed other machine learning models with an accuracy of 99.01%. Clustering of employees is performed according to the key performance indicators using the K-Means clustering algorithm, which opens up the trends in organizational productivity and employee engagement. The suggested system aligns with modern methodologies that support a comprehensive perspective of employee feedback by integrating sentiment classification, predictive modeling, and clustering into a single pipeline. Linking clustered behaviours to productivity and attendance trends goes beyond simple sentiment polarity and enables organizations to pinpoint not only disgruntled individuals but also systemic problems across departments.