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Predicting Career Trends in Vietnam's Labor Market Through News-Based Sentiment Analysis
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Author(s): Ha Thi Thu Nguyen (FPT University, Vietnam), Hoang Anh Tuan (Thuongmai University, Vietnam), Le Thi Tram Anh (Thuongmai University, Vietnam), Vu Ngoc Tu (Thuongmai University, Vietnam), Duong Nguyen Thanh Thuy (Thuongmai University, Vietnam)and Long Duc Nguyen (Vietnam Academy of Science and Technology, Vietnam)
Copyright: 2026
Volume: 17
Issue: 1
Pages: 22
Source title:
International Journal of Asian Business and Information Management (IJABIM)
Editor(s)-in-Chief: Patricia Ordóñez de Pablos (Universidad de Oviedo, Spain)
DOI: 10.4018/IJABIM.402698
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Abstract
Traditional forecasting methods that depend on official surveys and macroeconomic data often lag behind real-world developments, missing early indicators of change. To address this gap, this study utilizes unstructured Vietnamese news articles as an alternative information source. By applying natural language processing and sentiment analysis, it extracts employment-related signals of optimism or concern and converts them into predictive features. The proposed occupation sentiment ranking algorithm integrates occupation extraction and sentiment analysis to detect real-time shifts in labor market dynamics. Empirical findings reveal that news-based sentiment significantly enhances prediction accuracy and responsiveness, particularly during times of disruption. This research provides a timely and effective early-warning framework for Vietnam's labor market, offering actionable insights for policymakers, universities, and job seekers navigating the challenges of the digital economy.
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