Harnessing NLP to align Candidate Profile with Position Requirements

To showcase one’s talents and experience, using a resume is generally one of the more used and accepted approaches. It is usually written in several file formats (pdf, txt, doc, etc.) and in a variety of inconsistent styles with distinct personalized approaches. Job ads in the current digital era draw a flood of applications in a short amount of time. Hiring organizations incur cost challenges in addition to time-consuming delays when sorting through resumes manually. Selecting a candidate for a job solely on the basis of whether their resume matches a list of qualifications is usually a difficult, demanding, time-consuming, and effort-intensive process. Furthermore, the conventional screening procedure frequently lacks impartiality, which causes potentially qualified applicants to be passed over or unfit individuals to be chosen. Leveraging Natural Language Processing (NLP), in order to classify resumes into predetermined job categories, this study investigates the use of three machine learning algorithms: Bidirectional Long Short-Term Memory (Bi-LSTM), Multinomial Naive Bayes (MNB), and K-Nearest Neighbors (KNN). To convert textual data into numerical form, a large dataset of resumes annotated with job categories is first pre-processed and vectorized using TF-IDF. Next, the efficacy of KNN, MNB, and Bi-LSTM models in classifying resumes according to their content is trained and assessed. After the classification phase, the cosine similarity score between the resume and job description vectors is computed using the trained models to match resumes with job descriptions. This two-step method seeks to improve the accuracy of matching candidates with appropriate job roles and streamline the hiring process by automating resume classification.