An Intelligent Text Mining Framework for Decision Support in Government Hiring

Government hiring processes face significant challenges in efficiently and equitably matching qualified candidates with appropriate positions while managing high application volumes. This paper presents an Intelligent Text Mining Framework for decision support in government hiring that integrates lexical and semantic similarity pathways through a weighted fusion mechanism. The framework processes resume and job description corpora using parallel TF-IDF (lexical) and Sentence-BERT (semantic) analyses, combines scores via a tunable parameter α, and ranks candidates per job description. A comprehensive evaluation on a dataset of 100 resumes and 10 job descriptions demonstrates that the hybrid approach achieves a mean combined similarity score of 0.642 ± 0.113 with high reliability (split half correlation r=0.891, p