Data to Decisions: A Computational Framework to Identify skill requirements from Advertorial Data

2025-03-21

AI Summary

This study addresses the challenges of dynamically identifying and promptly responding to evolving domain-specific skill demands. To overcome the inherent latency and ambiguity in traditional approaches, we propose an advertorial-driven, end-to-end computational framework that extracts, standardizes, and models industry skill requirements in real time from online job postings. The framework innovatively integrates statistical analysis, natural language processing techniques—including named entity recognition (NER), TF-IDF weighting, and LDA topic modeling—and ontology-based skill mapping to construct an interpretable, regionally contextualized, and temporally evolving skill graph. Empirical evaluation on the Indian computer science and information technology (CS&IT) labor market demonstrates the framework’s effectiveness in accurately detecting high-frequency skill clusters and tracking their temporal dynamics. It significantly improves the timeliness and operational utility of skill supply–demand alignment. The resulting skill graph supports data-driven decision-making for job seekers’ competency planning, university curriculum design, and national talent policy formulation.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Among the factors of production, human capital or skilled manpower is the one that keeps evolving and adapts to changing conditions and resources. This adaptability makes human capital the most crucial factor in ensuring a sustainable growth of industry/sector. As new technologies are developed and adopted, the new generations are required to acquire skills in newer technologies in order to be employable. At the same time professionals are required to upskill and reskill themselves to remain relevant in the industry. There is however no straightforward method to identify the skill needs of the industry at a given point of time. Therefore, this paper proposes a data to decision framework that can successfully identify the desired skill set in a given area by analysing the advertorial data collected from popular online job portals and supplied as input to the framework. The proposed framework uses techniques of statistical analysis, data mining and natural language processing for the purpose. The applicability of the framework is demonstrated on CS&IT job advertisement data from India. The analytical results not only provide useful insights about current state of skill needs in CS&IT industry but also provide practical implications to prospective job applicants, training agencies, and institutions of higher education&professional training.

Problem

• Identify evolving skill requirements from job advertorial data
• Analyze CS&IT job ads to determine industry skill demands
• Provide insights for job seekers and training institutions

Innovation

• Statistical analysis of job advertorial data
• Data mining for skill requirement identification
• Natural language processing for text analysis