The rapid advancement of digital technologies is transforming human resource (HR) functions, especially recruitment, where efficiency and fairness are increasingly prioritized. Traditional recruitment practices, involving manual resume screening and subjective interview assessments, are time-consuming, susceptible to bias, and often lack the precision required in a competitive hiring environment. Addressing these limitations, there is a growing demand for innovative, automated approaches that enhance recruitment accuracy and reduce bias. Although Natural Language Processing (NLP) offers promising solutions, existing research often relies on basic keyword matching and sentiment analysis, which do not fully capture the nuanced context or emotional assessments necessary for comprehensive candidate evaluation. This study aims to bridge this gap by developing an advanced NLP framework that combines BERT-based semantic embedding for contextual resume screening and VADER-based sentiment analysis to evaluate candidates’ emotional responses in interviews. Using a sample dataset of 1,000 resumes and 500 interview responses, our model demonstrated high precision (89%) in candidate-job matching and consistent emotional assessment scores, surpassing traditional human-led evaluations in speed and fairness. The findings underscore NLP’s potential to enhance recruitment practices by improving efficiency, reducing subjective bias, and providing data-driven insights for HR professionals. This study contributes to HR practices by advocating the adoption of NLP in recruitment, promoting a fairer, more effective approach to talent acquisition that aligns with the trend toward intelligent, data-centric HR solutions.