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Human Systems Management - Volume 37, issue 4

Purchase individual online access for 1 year to this journal.

Price: EUR 130.00
ISSN 0167-2533 (P)
ISSN 1875-8703 (E)

Impact Factor 2024: 1.5 

Human Systems Management (HSM) is an interdisciplinary, international, refereed journal. It addresses the need to mentally grasp and to in-form the managerial and societally organizational impact of high technology, i.e., the technology of self-governance and self-management.

The gap or gulf is often vast between the ideas world-class business enterprises and organizations employ and what mainstream business journals address. The latter often contain discussions that practitioners pragmatically refute, a problematic situation also reflected in most business schools’ inadequate curriculæ.

To reverse this trend, HSM attempts to provide education, research and theory commensurate to the needs to today’s world-class, capable business professionals. Namely the journal’s purposefulness is to archive research that actually helps business enterprises and organizations self-develop into prosperously successful human systems.

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Keyword extraction from social media via AHP

Authors: Ramay, Waheed Yousuf | Cheng-Yin, Xu | Illahi, Inam

Article Type: Research Article

Abstract: In the context of natural-language processing, keyword extraction has been studied widely. In promoting business-enterprise goods and services, however, a major challenge remains to extracting keywords effectively and efficiently from social-media user-generated data, wherein employed are traditional, language-dependent and supervised keyword-extraction techniques. This study contributes a keyword extraction analytic hierarchy process (KEAHP), as a language-independent and unsupervised keyword-extraction technique. By using four user-generated data attributes, KEAHP identifies keywords from the word co-occurrence in linguistic networks, based on a multiple-attribute decision-making approach. The proposed technique has been validated via a publically-available standard dataset, and the experimental results show the effectiveness and … Show more

Keywords: Analytic hierarchy process, social media data, keyword extraction, KEAHP, word co-occurrence network, event detection

DOI: 10.3233/HSM-180344

Citation: Human Systems Management, vol. 37, no. 4, pp. 463-468, 2018

Price: EUR 27.50
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