Application of Text Mining Techniques on Scholarly Research Articles: Methods and Tools

Abstract

A vast amount of published scholarly literature is generated every day. Today, it is one of the biggest challenges for organisations to extract knowledge embedded in published scholarly literature for business and research applications. Application of text mining is gaining popularity among researchers and applications are growing exponentially in different research areas. This study investigates the variety of text mining tools, techniques, sample sizes, domains and sections of the documents preferred by the text mining researchers through a systematic and structured literature review of conceptual and empirical studies. The significant findings depict that LDA and R package is the most extensively used tool and technique among the authors, most of the researchers prefer the sample size of 1000 articles for analysis, literature belonging to the domain of ICT, and related disciplines are frequently analysed in the text mining studies and abstracts constitute the corpus of the majority of text mining studies.

Keywords:

• knowledge discovery
• Latent Dirichlet Allocation
• research trends analysis
• text mining
• topic modelling