ResumeVis: A Visual Analytics System to Discover Semantic Information in Semi-structured Resume Data
This work addresses the need for comprehensive career development analysis in talent recruitment and evaluation, offering a domain-specific tool for HR professionals and analysts.
The authors tackled the problem of discovering implicit semantic information like career progress patterns and social relations in semi-structured resume data, which existing statistical methods missed, by proposing ResumeVis, a visual analytics system that mines and visualizes this information, demonstrated with over 2500 online officer resumes.
Massive public resume data emerging on the WWW indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as talent seeking and evaluation. Existing RA studies based on statistical analyzing have primarily focused on talent recruitment by identifying explicit attributes. However, they failed to discover the implicit semantic information, i.e., individual career progress patterns and social-relations, which are vital to comprehensive understanding of career development. Besides, how to visualize them for better human cognition is also challenging. To tackle these issues, we propose a visual analytics system ResumeVis to mine and visualize resume data. Firstly, a text-mining based approach is presented to extract semantic information. Then, a set of visualizations are devised to represent the semantic information in multiple perspectives. By interactive exploration on ResumeVis performed by domain experts, the following tasks can be accomplished: to trace individual career evolving trajectory; to mine latent social-relations among individuals; and to hold the full picture of massive resumes' collective mobility. Case studies with over 2500 online officer resumes demonstrate the effectiveness of our system. We provide a demonstration video.