DECODING RESUMES: THE DATA-DRIVEN APPROACH TO TALENT ACQUISITION
Keywords:
Resume Categorization, Talent Acquisition, Data Science, Natural Language Processing, Latent Dirichlet Allocation, Recruitment TechnologyAbstract
The process of talent acquisition has been subject to a profound transformation due to developments in the field of data science and technology. In this paper begin by discussing the importance of resume categorization in identifying qualified candidates. We also discuss the challenges faced in scrutinizing unstructured resumes and the advantages offered by machine learning and natural language processing techniques in this domain. Then we discuss the significance of resume categorization, its role in streamlining the initial screening process, and its contribution to strategic talent pool management. We also explore the unique advantages of Latent Dirichlet Allocation (LDA) topic modeling over other classification-based approaches. We train a LDA model and use it to extract the topic distribution within the dataset and the keyword distribution within each of the topics. Finally, we demonstrate the effectiveness of our approach by visualizing topics on an inter-topic distance map by highlighting the important keywords for a given topic.
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Copyright (c) 2023 Akshata Upadhye (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
