Predicting Employee Job Satisfaction through Explainable Machine Learning - A Text-Analytic Study of Glassdoor Reviews -

[Purpose] This study proposes a novel explainable AI framework designed to predict employee job satisfaction from online reviews. Explicitly highlighting the study’s novelty, the framework integrates topic-level semantics(Latent Dirichlet Allocation(LDA)) and LLM-based linguistic diversity indicators to reveal how employees’ narrative complexity reflects their underlying job satisfaction. This approach moves beyond traditional feature engineering to capture the subtle linguistic-psychological patterns. [Methodology]A total of 141,854 Glassdoor reviews from 120 firms were analyzed through four stages: data collection, preprocessing, modeling, and evaluation. Ratings of 4–5 were labeled as satisfied and 3 or below as dissatisfied. LDA topic modeling identified three themes from pros and cons sections, while linguistic diversity indicators such as cons_entropy, cons_sem_div, cons_pos_ttr, and cons_mtld were extracted using Sentence-BERT and MPNet embeddings. Random Forest and XGBoost models were trained with Synthetic Minority Over-sampling Technique(SMOTE)-based balancing, and SHapley Additive exPlanations(SHAP) analysis was applied for interpretability. [Findings]Random Forest achieved the best performance(accuracy and F1 = 0.85). Key predictors included employees’ company recommendations, CEO evaluations, business outlook perceptions, and linguistic diversity measures capturing complex or coherent expression patterns. [Implications]Results support the Job Demands-Resources(JD-R) model, showing that language reflecting workload and conflict lowers satisfaction, while positive references to pay and support enhance it. The explainable AI framework offers theoretical and practical insights for transparent HR analytics.