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Towards Job Screening and Personality Traits Estimation From Video Transcriptions
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  • Yazid BOUNAB ,
  • Mourad Oussalah ,
  • Nabil Arhab ,
  • Salah Eddine Bekhouche
University of Oulu

Corresponding Author:[email protected]

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Mourad Oussalah
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Nabil Arhab
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Salah Eddine Bekhouche
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The paper built on First Impression Challenge from Chalearn V2 Workshop on Explainable Computer Vision Multimedia and Job Candidate Screening Competition CVPR17 by focusing solely on Textual Input in contrast to other Challenge’s participants who considered video or audio modalities. Therefore, the paper aims to develop a new deep learning architecture capable of predicting human personality traits and job interview from the video transcripts. Several feature representations that involve statistical and deep learning have contrasted. Our approach achieved the best score when text modality alone were employed, yielding an average of 89% score in human personality traits and 89.10% value for job interview. The research results will help companies and other organization studying human personality to assess a human personality using a minimum textual resources from the job candidates
Mar 2024Published in Expert Systems with Applications volume 238 on pages 122016. 10.1016/j.eswa.2023.122016