The NLP for Employee Review Sentiment Analysis: An Explainable Perspective

Main goal of the research presented in this manuscript is to analyze employees satisfaction by employing hybrid methods between machine learning and metaheuristics. Furthermore, the proposed work utilizes term frequency-inverse document frequency (TF-IDF), natural language processing (NLP) method, for the identification of important segments of the employees' statements towards their workplaces, while the extreme gradient boosting (XGBoost) model is used for predicting the outcomes. Since each machine learning technique should be calibrated to particular problem, the XGBoost has been optimized by a modified swarm metaheuristics based on red fox optimizer (RFO) algorithm and its performance is evaluated against other high-performing swarm intelligence approaches. The results indicate the superiority of proposed solution, which has been further evaluated with the Shapley additive explanations (SHAP) analysis.