AI - Resume Analyzer

References

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@article{1712737,
      author = {Aaliya Nashath, Aqsa Jabeen, Umama Hannan, Sindhu C. R., Ibrahim Khaleelulla},
      title = {AI - Resume Analyzer},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {698-704},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712737.pdf},
      abstract = {In the evolving digital recruitment ecosystem, automation has become essential to handle the increasing volume of job applications and resumes. This paper presents an AI-driven Resume Analyzer, that employs Natural Language Processing (NLP) and Machine Learning (ML) techniques to automate the process of resume screening, skill extraction, and data visualization. The proposed system utilizes the Pyresparser library to parse unstructured resume data and extract critical attributes such as candidate information, educational background, work experience, and technical skills. Extracted data is stored in a structured MySQL database and visualized using Plotly through an interactive Streamlit interface to provide real-time analytics. Furthermore, the system introduces a LinkedIn-integrated Job Search module, which dynamically redirects users to relevant job openings based on the extracted skills and experience. This integration bridges the gap between resume evaluation and active job discovery, enabling users to explore employment opportunities instantly. Experimental evaluations demonstrate that the system achieves high accuracy and efficiency in parsing resumes while reducing manual screening time by over 80%. The proposed approach provides a lightweight, scalable, and intelligent recruitment framework that enhances transparency, fairness, and data-driven decision-making in modern human resource management.},
      keywords = {Artificial Intelligence (AI), Natural Language Processing (NLP), Resume Parsing, Machine Learning, Recruitment Automation, LinkedIn Integration},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712737}
  }