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In this paper, we propose a method for resume rating using Latent DirichletAllocation (LDA) and entity detection with SpaCy. The proposed method firstextracts relevant entities such as education, experience, and skills from theresume using SpaCy's Named Entity Recognition (NER). The LDA model then usesthese entities to rate the resume by assigning topic probabilities to eachentity. Furthermore, we conduct a detailed analysis of the entity detectionusing SpaCy's NER and report its evaluation metrics. Using LDA, our proposedsystem breaks down resumes into latent topics and extracts meaningful semanticrepresentations. With a vision to define our resume score to be morecontent-driven rather than a structure and keyword match driven, our model hasachieved 77% accuracy with respect to only skills in consideration and anoverall 82% accuracy with all attributes in consideration. (like college name,work experience, degree and skills)

Resume Evaluation through Latent Dirichlet Allocation and Natural Language Processing for Effective Candidate Selection