Using a hybrid methodology for literature review: a case study in depression research

Document Type

Article

Source of Publication

Information Discovery and Delivery

Publication Date

1-1-2023

Abstract

Purpose: This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective. Design/methodology/approach: This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review. Findings: The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure. Originality/value: This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

ISSN

2398-6247

Publisher

Emerald

Disciplines

Computer Sciences

Keywords

Big data analytics, Depression care, Literature review, Major depressive disorder, Semantic analysis, Text mining

Scopus ID

85175870084

Indexed in Scopus

yes

Open Access

no

Link to Full Text

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