Resume Classification System using Natural Language Processing and Machine Learning Techniques

Abstract

The selection of a suitable job applicant from the pool of thousands applications is often daunting job for an employer. The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process. Moreover, the automation of this process can significantly expedite and transparent the applicants’ screening process with mere human involvement. This experimental study presents an automated NLP and ML-based RCS that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of RCS and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP and ML techniques for RCS, the extracted features were evaluated on nine ML classification models namely Support Vector Machine - SVM (Linear, SGD, SVC and NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The Term-Frequency-Inverse-Document-Frequency (TF-IDF) feature representation scheme was proved suitable for RCS. The developed models were evaluated using the Confusion Matrix, F-Score, Recall, Precision, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume classification task, the SVM class of Machine Learning classifiers performed better on the study dataset of over nine hundred sixty plus parsed resumes with more than 96% accuracy. The promising results suggest that NLP and ML techniques employed in this study could be used for developing an efficient RCS.

Anemia detection through non-invasive analysis of lip mucosa images

“…Precision measures the proportion of true positives out of all positive predictions made by the model and is useful when the goal is to minimize false positives (Juba and Le, 2019 ; Miao and Zhu, 2022 ). Recall, on the other hand, measures the proportion of true positives out of all the actual positive examples in the data, and is useful when the goal is to minimize false negatives (Ali et al, 2022 ; Miao and Zhu, 2022 ). F1 score is the harmonic mean of precision and recall and is useful to balance the importance of both (Hossin and Sulaiman, 2015 ).…”

Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks

“…When minimizing false positives, precision indicates the percentage of true positives across all positive predictions provided by the model (Juba and Le, 2019;Miao and Zhu, 2022). Contrarily, recall assesses the proportion of real positives among all of the actual positive instances in the data and is helpful when the objective is to reduce false negatives (Ali et al, 2022;Miao and Zhu, 2022). F1 score is the harmonic mean of precision and recall and is useful to balance the importance of both (Hossin and Sulaiman, 2015).…”

Análise Comparativa entre Abordagens de Aprendizado de Máquina para Classificação Automática de Currículos de Profissionais de TIC

“…Nos últimos anos, as técnicas de classificac ¸ão de texto baseadas em algoritmos de Aprendizado de Máquina (AM) têm sido amplamente utilizadas em diversos domínios [Ali et al 2022].…”

“…Nesse contexto, o trabalho de [Gopalakrishna and Vijayaraghavan 2019] propôs um sistema de classificac ¸ão de currículos que utiliza um conjunto de seis algoritmos tradicionais de AM (Naive Bayes, Naive Bayes multinomial, Bernoulli Naive Bayes, Máquinas de Vetores de Suporte, Regressão Logística e K-vizinhos Mais Próximos). Outro trabalho que utiliza algoritmos de AM nesse contexto é o desenvolvido por [Ali et al 2022], que compara o desempenho de nove algoritmos para classificac ¸ão de currículos.…”

“…Alguns desses trabalhos, como [Satheesh et al 2020, Najjar et al 2021, Ransing et al 2021, abordam esse tema atribuindo uma pontuac ¸ão aos currículos com base em uma descric ¸ão de vaga fornecida pelo sistema, utilizando algoritmos de AM. Outras pesquisas, como em [Roopesh and Babu 2021, Rajath et al 2021, Ali et al 2022, exploram algoritmos de AM para classificac ¸ão, buscando determinar se um currículo possui ou não os elementos necessários que o credenciam para uma determinada vaga.…”