# Personality Detection of Applicants And Employees Using K-mode Algorithm And Ocean Model

**Authors:** Binisha Mohan, Dinju Vattavayalil Joseph, Bharat Plavelil Subhash  
**arXiv:** [2212.14675](https://arxiv.org/abs/2212.14675)  
**Date:** 2022-12-27  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2212.14675v1/

## Key points

- Personality is a combination of conduct, emotion, motivation, and thinking that plays a crucial role in hiring the right candidate for a job.
- Many organizations rely on personality predictions to shortlist candidates more effectively.
- A model is created to identify applicants' personality types so that employers may find qualified candidates by examining a person's facial expression, speech intonation, and resume.
- Detecting changes in employee behavior can help improve employee quality and retention.
- Research efforts have discussed various aspects of personality prediction using text, image, and video analytics.
- One study found that this model of personality can be used to evaluate job performance criteria across different occupational groups.
- Another proposed a prototype method known as the Applicant Personality Prediction System Utilizing Machine Learning (APPS) to aid HR departments in certain organizations employ or select the best aspirant for the favored job function.
- The Heterogeneous Information Ensemble (HIE) framework was employed to approximate consumers' personality characteristics using diverse information such as identity, likeness, symbols, and replying attitudes.
- Writers also employed Text-CNN, Correlation analysis and pouch clustering to analyze written text.
- Overall findings imply that AVIs can be used for efficient candidate screening with an AI decision agent; however deeper models and new configurations are needed for extremely complex operations.

## Summary

Personality is a combination of conduct, emotion, motivation, and thinking that plays a crucial role in hiring the right candidate for a job. To shortlist candidates more effectively, many organizations rely on personality predictions. A model is created to identify applicants' personality types so that employers may find qualified candidates by examining a person's facial expression, speech intonation, and resume. Additionally, detecting changes in employee behavior can help improve employee quality and retention. Research efforts have discussed various aspects of personality prediction using text, image, and video analytics. One study found that this model of personality can be used to evaluate job performance criteria across different occupational groups. Another proposed a prototype method known as the Applicant Personality Prediction System Utilizing Machine Learning (APPS) to aid HR departments in certain organizations employ or select the best aspirant for the favored job function. APPS evaluated the characters of applicants relying on the personality test provided during the registration process. The Heterogeneous Information Ensemble (HIE) framework was employed to approximate consumers' personality characteristics using diverse information such as identity, likeness, symbols, and replying attitudes. It addressed whether self-language is the only valuable factor of a person's personality by utilizing psychological lexicon LIWC to determine a user's personality based on how words were employed in different semantic categories. In addition to cutting-edge lexicon-based methods like LIWC, writers also employed Text-CNN, Correlation analysis and pouch clustering to analyze written text. The standard CNN structure has limited ability to read messages because there is no clear link between adjoining dimensions in ingrained word vectors; however authors have applied pooling layer and max pooling layers using visual models to address this problem. Overall findings imply that AVIs can be used for efficient candidate screening with an AI decision agent; however deeper models and new configurations are needed for extremely complex operations. By utilizing logistic regression techniques such as APPS or HIE frameworks along with natural language processing automation and open-vocabulary reaches to natural language recognition employers can better predict a person's character based on how they talk during an interview process.