Detecting Subjectivity in Staff Perfomance Appraisals by Using Text Mining: Teachers Appraisals of Palestinian Government Case Study

Abed, Amani A.; El-Halees, Alaa M.

doi:10.1109/picict.2017.25

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…Novel applications of ML within performance management include detection of subjectivity in performance appraisal process using text analysis and natural language processing (Abed and El-Halees, 2017), estimation of expertise level of employees using data mining and ordinal regression clustering (Horesh et al , 2016), analysing the impact of financial incentives on efficiency of employees using classification algorithms (Massrur et al , 2014) and profiling of employees to develop customized incentives using classification technique (Petruzzellis et al , 2006).…”

Section: Detailed Analysis Of the Resultsmentioning

confidence: 99%

A review of machine learning applications in human resource management

Garg

Sinha

Kar

et al. 2021

IJPPM

166

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PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

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Section: Detailed Analysis Of the Resultsmentioning

confidence: 99%

A review of machine learning applications in human resource management

Garg

Sinha

Kar

et al. 2021

IJPPM

166

View full text Add to dashboard Cite

show abstract

“…Supervised and lexicon-based [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]. Unsupervised and lexicon-based [62], [63], [64].…”

Section: Learning Techniques Papersmentioning

confidence: 99%

“…DT [7], [16], [18], [19], [24], [25], [41], [56], [59]. SVM [2], [7], [8], [9], [12], [15], [16], [17], [19], [20], [21], [24], [25], [38], [41], [48], [51], [52], [56], [57], [59], [60], [64]. KNN [9], [15], [18], [19], [23], [52], [64].…”

Section: Supervised Learning Models Papersunclassified

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Oghu,

Ogbuju,

Abiodun

et al. 2023

Bull.Socinf.The.App

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The education industry considers quality to be a crucial factor in its development. Nevertheless, the quality of many institutions is far from perfect, as there is a high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, the literature lacks a reasonable number of systematic reviews that classify research that applied natural language processing and machine learning solutions for students’ sentiment analysis and quality assurance feedback. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in a high-impact journal-indexed database. The work extracted 59 relevant papers from the 3392 initially found using exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in education and the prevalent supervised machine learning algorithms, lexicon-based approaches, and evaluation metrics in assessing feedback in the education domain.

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“…The research studies a big amount of text originated from peer analysis and proposes a way to identify certain aspects that would be difficult to appear on a superficial analysis. The work of [18] and [19] also present aspects regarding the use of text analysis in a human resource manage context. [18] presents an approach for team member selections based on contextual sentiment closeness.…”

Section: A Bibliographic Review and Related Workmentioning

confidence: 99%

“…[18] presents an approach for team member selections based on contextual sentiment closeness. The work of [19] presents an approach to detect subjectivity on teacher's performance trough text analysis. The work of [20], by other hand, presents a strategy for text classification that adopts a Bagging ensemble classifiers strategy based on a genetic algorithm.…”

Section: A Bibliographic Review and Related Workmentioning

confidence: 99%

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

Goes

Oliveira

2020

IEEE Access

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This paper proposes a process for human resource performance evaluation using computational intelligence techniques. The human resource (or employee's) performance evaluation is essentially a regular assessment and review of an employee's performance on the job. This evaluation can be performed in different ways, depending on the kind of job of the employee and on the company's politics or business area. The process proposed on this research combines Fuzzy logic, text sentiment analysis and supervised learning classification techniques, such as a multi layer perceptron artificial neural network, decision tree algorithms and naïve bayes into ensemble classifiers, in an attempt to provide a fair evaluation process, minimizing or even eliminating common problems caused by simple objective or subjective approaches. The data provided for this research was originated from several evaluations applied in two Brazilians institutions. Simulation results shows consistence on the data generated by this proposed process, indicating a good perspective for applications on companies of most business areas.

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Detecting Subjectivity in Staff Perfomance Appraisals by Using Text Mining: Teachers Appraisals of Palestinian Government Case Study

Cited by 7 publications

References 9 publications

A review of machine learning applications in human resource management

A review of machine learning applications in human resource management

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

A Process for Human Resource Performance Evaluation Using Computational Intelligence: An Approach Using a Combination of Rule-Based Classifiers and Supervised Learning Algorithms

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