Personality Forecast Through Resume Analysis and Aptitude Test Using NLP

Author

Diwauripo, Don

Title

Personality Forecast Through Resume Analysis and Aptitude Test Using NLP

Abstract

This study aimed to analyse various methods and models used in resume personality prediction using Natural Language Processing (NLP), develop and implement an NLP model for CV analysis and aptitude testing to assess candidate suitability, and evaluate the effectiveness of combining CV analysis and aptitude tests in predicting personality traits compared to traditional methods. Chapter 2 provided a comprehensive review of relevant literature, enabling the researcher to gain insights into different models and NLP techniques applicable to personality prediction using CVs and aptitude tests. Using the rapid prototyping model, Python, and Flask framework, the researcher successfully developed a system that accurately determined the personality of applicants and made hiring decisions based on the obtained results. Consequently, all the objectives of this study were achieved. To assess the model's performance, a confusion matrix was employed, which consisted of 100 samples, half representing successful candidates (positive) and the other half representing unsuccessful candidates (negative). The results indicated that the model achieved an 80% accuracy rate in correctly identifying successful candidates (true positives) and a 10% false positive rate for unsuccessful candidates. Furthermore, the model accurately identified 85% of unsuccessful candidates (true negatives) and had a 5% false negative rate for successful candidates. These findings demonstrate the high accuracy of the predictive model in forecasting candidate suitability and suggest its potential to assist organizations in making informed decisions regarding personnel selection.

Date

2023

Publisher

BUSE

Keywords

Personality Forecast

Resume Analysis and Aptitude Test

Supervisor

N/A