Research on employment sentiment analysis of college graduates based on global and local key fusion features
Meishuang Ding, Xingping Qin, Hongmin Li, Chen DingPurpose
The increasingly competitive job market has intensified psychological pressure on college graduates, posing new challenges for intelligent analysis and intervention within cyber-physical-social systems (CPSS). To address the limitations of manual assessment and support collaborative intelligence in employment counseling scenarios, this paper aims to propose an employment sentiment analysis method based on global and local key feature fusion.
Design/methodology/approach
By incorporating rough set theory, a local key information module is constructed to identify sentiment-critical words from interview data sets, enabling interpretable feature selection suitable for edge-level processing. Furthermore, convolutional neural networks are employed to extract sentiment representations at both global and local scales, while a channel attention mechanism is introduced to enhance feature discrimination without increasing model complexity, making the approach suitable for edge-artificial intelligence deployment. An overlapping pooling strategy combined with multilevel pyramids is adopted to improve robustness under resource-constrained environments. Finally, global and local sentiment information is collaboratively fused to support multidimensional sentiment analysis within CPSS.
Findings
Experimental results demonstrate that the proposed method effectively captures employment-related emotional semantics of college graduates and provides accurate early warning of negative sentiment, offering practical value for enabling collaborative intelligence-driven interventions in smart employment and social support systems.
Originality/value
This study introduces a novel employment sentiment analysis method that fuses global and local key features to improve the accuracy of detecting emotional states in college graduates seeking jobs. By integrating rough set-based key word selection, CNN-based multiscale sentiment mining, channel attention and multilevel pyramid pooling, the approach enhances feature extraction without increasing model complexity. The fusion of global text context and local word-level cues enables a more nuanced and reliable assessment, providing timely warning of negative emotions and supporting targeted manual intervention in graduate employment counseling.