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<p>The human resources department enhances employee engagement through programs promoting satisfaction, recognition, and growth. The inclusion of artificial intelligence (AI) in human resource management (HRM) is revolutionizing employee engagement and work performance tactics via sophisticated data-driven insights. This study examines the use of natural language processing (NLP) in human resources, utilizing Latent Dirichlet Allocation (LDA) for topic modeling on a dataset of 1111&#xa0;research publications from 1984 to 2024. The report delineates rising patterns in employee engagement, work performance, and AI-driven human resource initiatives. NLP techniques, including sentiment analysis, chatbot communication, and automated feedback analysis, are crucial to contemporary HR applications; the study also emphasizes larger AI contributions, such as predictive analytics for retention and AI-augmented decision-making. A k-means clustering method was employed to validate the five-topic model, resulting in a coherence score of 0.53, while manual expert assessment further confirmed topic interpretability. The results offer practical insights for further research on AI-driven HR innovation, highlighting the influence of NLP in comprehending employee experiences and enhancing workforce management.</p>

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The intersection of artificial intelligence and human resources: transforming journey using natural language processing

  • 期刊论文

  • 发表日期：

    2025年4月3日

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  • Chetan Sharma,
  • Nisha Chanana

摘要

The human resources department enhances employee engagement through programs promoting satisfaction, recognition, and growth. The inclusion of artificial intelligence (AI) in human resource management (HRM) is revolutionizing employee engagement and work performance tactics via sophisticated data-driven insights. This study examines the use of natural language processing (NLP) in human resources, utilizing Latent Dirichlet Allocation (LDA) for topic modeling on a dataset of 1111 research publications from 1984 to 2024. The report delineates rising patterns in employee engagement, work performance, and AI-driven human resource initiatives. NLP techniques, including sentiment analysis, chatbot communication, and automated feedback analysis, are crucial to contemporary HR applications; the study also emphasizes larger AI contributions, such as predictive analytics for retention and AI-augmented decision-making. A k-means clustering method was employed to validate the five-topic model, resulting in a coherence score of 0.53, while manual expert assessment further confirmed topic interpretability. The results offer practical insights for further research on AI-driven HR innovation, highlighting the influence of NLP in comprehending employee experiences and enhancing workforce management.