Sentiment Classification and Prediction of Job Interview Performance

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

External organisations

  • University of Birmingham

Abstract

Attracting and hiring talented employees is a challenge for companies. The job interview process is a very critical step for both employer and candidate. Having a smooth hiring process in a company will increase future employees' satisfaction. Candidates tend to share their feedback and experience of interviews and company's hiring process with others. Having a negative experience can affect its brand image and reputation as an employer. This will make it hard to attract talented employees. In this research, machine learning and neural network models, such as support vector machines, logistic regression, Naïve Bayes, and long short-term memory (LSTM), were trained to predict the candidates' sentiments after a job interview. Each model was trained using several data representations and weighting approaches, such as term binary, term frequency, and term frequency-inverse document frequency (TF-IDF). As a result, training logistic regression with TF-IDF and unigram word representation achieved an F1-measure of 0.814.

Details

Original languageEnglish
Title of host publication2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
Publication statusPublished - May 2019
Event2nd International Conference on Computer Applications and Information Security, ICCAIS 2019 - Riyadh, Saudi Arabia
Duration: 1 May 20193 May 2019

Publication series

Name2nd International Conference on Computer Applications and Information Security, ICCAIS 2019

Conference

Conference2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
CountrySaudi Arabia
CityRiyadh
Period1/05/193/05/19

Keywords

  • Continues bag of words, K nearest neighbour, Logistic regression, Long short term memory, Naïve Bayes, Random forest, Skip gram, Support vector machine