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Introduction:

In today’s digital age, the recruitment process has become increasingly automated with the use of technology such as applicant tracking systems (ATS). These systems are designed to scan and filter resumes based on predefined criteria set by employers. However, the traditional keyword-based approach used by ATS may not always effectively evaluate a candidate’s qualifications and skills. This has led to the emergence of natural language processing (NLP) as a potential solution for automating resume screening and ranking.

The objective of this thesis is to investigate the use of NLP techniques for automated resume screening and ranking. By leveraging NLP, we aim to develop a more sophisticated and accurate system that can effectively parse and analyze the content of resumes to identify the most qualified candidates for a given job position.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Evolution of Recruitment Process
2.2 Traditional Resume Screening Techniques
2.3 Limitations of Keyword-Based ATS
2.4 Introduction to Natural Language Processing
2.5 NLP Applications in Recruitment
2.6 NLP Techniques for Resume Parsing
2.7 NLP for Resume Ranking
2.8 Challenges and Opportunities in NLP for Recruitment
2.9 Previous Studies on NLP in Recruitment
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 NLP Model Development
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Statistical Analysis
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of NLP Model Performance
4.2 Comparison with Traditional ATS
4.3 Impact on Recruitment Process
4.4 Recommendations for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Limitations and Future Directions

Thesis Overview:

The use of natural language processing (NLP) in the recruitment process has gained significant attention in recent years. This thesis aims to investigate the effectiveness of NLP techniques for automated resume screening and ranking. By leveraging NLP, we seek to develop a more sophisticated and accurate system that can analyze the content of resumes to identify the most qualified candidates for a given job position.

Chapter 1 provides an introduction to the topic, including background information, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive review of the literature on the evolution of recruitment processes, traditional resume screening techniques, limitations of keyword-based ATS, NLP applications in recruitment, and previous studies on NLP in recruitment.

Chapter 3 outlines the research methodology, including research design, data collection, data preprocessing, NLP model development, evaluation metrics, experimental setup, statistical analysis, and ethical considerations. Chapter 4 discusses the findings of the study, including the analysis of NLP model performance, comparison with traditional ATS, impact on the recruitment process, and recommendations for future research.

Finally, Chapter 5 provides a conclusion and summary of the project, highlighting the key findings, contributions, implications for practice, limitations, and future directions. This thesis aims to contribute to the existing literature on the use of NLP in recruitment and offer valuable insights for practitioners looking to enhance their recruitment processes.

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