Human resource (HR) departments increasingly face the complex task of identifying candidates who best align with organisational needs while reducing recruitment costs and time-to-hire. Traditional manual sourcing of applicants across multiple platforms, such as job portals and social media, has become inefficient in the era of data abundance. This study explores how artificial intelligence (AI) technologies, particularly machine learning (ML) and data analytics, are transforming recruitment and selection processes by enabling more objective, efficient and data-driven decision-making.
A systematic literature review (SLR) was conducted to map the intellectual structure of AI-driven recruitment research. From an initial pool of 1,444 publications indexed in Scopus, 155 met the inclusion criteria after rigorous screening. Latent Dirichlet allocation (LDA) topic modelling was applied to identify dominant research themes. In contrast, VADER sentiment analysis was applied to representative topic terms to generate exploratory weighted lexical-valence indicators and examine how evaluative language associated with these topics varied over time.
The analysis identified twenty themes, consolidated into five overarching research domains: (1) Skill-based assessment, (2) AI algorithms and techniques, (3) Candidate sourcing, (4) Industry-specific employment and (5) Employee lifecycle challenges. The topic-term sentiment results suggest a generally positive but cautious lexical-valence pattern in AI-enabled recruitment research between 2014 and 2024. However, because most scores were close to neutral under standard VADER thresholds, these findings are interpreted as exploratory indicators of evaluative language associated with topic terms rather than direct measures of authors' attitudes, contextual sentiment toward individual studies, or practical effectiveness of AI recruitment systems.
This study extends the understanding of AI adoption in HRM by combining topic modelling and topic-level sentiment analysis to reveal emerging trends, research gaps and practitioner implications. It contributes to both academia and practice by illuminating how ML and data analytics can be strategically leveraged to enhance fairness, efficiency, and predictive accuracy in recruitment, while also identifying areas where stronger validation, contextual analysis and human oversight remain necessary.
