Extracting LSA topics as features for text classifiers across different knowledge domains

Evangelopoulos, Nicholas; Amirkiaee, S. Yasaman

doi:10.1007/s11135-019-00954-x

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Evangelopoulos and Amirkiaee [29] use latent semantic analysis (LSA) to show how feature extraction can work in text data. Feature extraction is essential for the use of classifiers.…”

Section: Natural Language Processingmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Techniques Used for Analyzing the Voice of the Customer in the Quality 4.0 Era: A Systematic Review Using PRISMA Protocol

Elshennawy

2024

IJER

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“…Evangelopoulos and Amirkiaee [29] use latent semantic analysis (LSA) to show how feature extraction can work in text data. Feature extraction is essential for the use of classifiers.…”

Section: Natural Language Processingmentioning

confidence: 99%

“…Feature extraction is essential for the use of classifiers. The study from Evangelopoulos and Amirkiaee [29] uses an exploratory case study to answer their research questions. The use of LSA in a unified corpus and a separate corpus was analyzed.…”

Section: Natural Language Processingmentioning

confidence: 99%

Artificial Intelligence Techniques Used for Analyzing the Voice of the Customer in the Quality 4.0 Era: A Systematic Review Using PRISMA Protocol

Elshennawy

2024

IJER

View full text Add to dashboard Cite

“…The LSA has been broadly applied in diverse academic research, such as feature extraction for knowledge management (Evangelopoulos and Amirkiaee, 2020), the social impact of emerging technology (Kwon et al ., 2017), railroad equipment accidents (Williams and Betak, 2018) and online review sentiment (Zhang et al ., 2019). However, this paper is the first research applying LSA and relevant approaches to recruitment in the HR domain.…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based human resources recruitment system for business process management: using LSA, BERT and SVM

Tian

Pavur

Han

et al. 2022

BPMJ

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PurposeStudies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.Design/methodology/approachLSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.FindingsBased on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.Research limitations/implicationsThe technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.Practical implicationsThe novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.Originality/valueTo the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

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“…e authors in [35] defined the applicability of LSA in determining problems in aerospace science. e authors in [36] utilized the LSA to extract the features across different knowledge domains such as information systems and operations management. In [37], the authors studied the impact of technology-enhanced learning in higher education.…”

Section: Introductionmentioning

confidence: 99%

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

et al. 2021

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Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.

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Extracting LSA topics as features for text classifiers across different knowledge domains

Cited by 3 publications

References 21 publications

Artificial Intelligence Techniques Used for Analyzing the Voice of the Customer in the Quality 4.0 Era: A Systematic Review Using PRISMA Protocol

Artificial Intelligence Techniques Used for Analyzing the Voice of the Customer in the Quality 4.0 Era: A Systematic Review Using PRISMA Protocol

A machine learning-based human resources recruitment system for business process management: using LSA, BERT and SVM

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

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