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Journal of Computer Sciences and Applications. 2017, 5(2), 42-49
DOI: 10.12691/jcsa-5-2-1
Open AccessArticle

Characterisation of Academic Journal Publications Using Text Mining Techniques

Adebola K. Ojo1, and Adesesan B. Adeyemo1

1Computer Science Department, University of Ibadan, Ibadan, Nigeria

Pub. Date: June 19, 2017

Cite this paper:
Adebola K. Ojo and Adesesan B. Adeyemo. Characterisation of Academic Journal Publications Using Text Mining Techniques. Journal of Computer Sciences and Applications. 2017; 5(2):42-49. doi: 10.12691/jcsa-5-2-1

Abstract

The ever-growing volume of published academic journals and the implicit knowledge that can be derived from them has not fully enhanced knowledge development but rather resulted into information and cognitive overload. However, publication data are textual, unstructured and anomalous. Analysing such high dimensional data manually is time consuming and this has limited the ability to make projections and trends derivable from the patterns hidden in various publications. This study was designed to develop and use intelligent text mining techniques to characterise academic journal publications. Journals Scoring Criteria by nineteen rankers from 2001 to 2013 of 50th edition of Journal Quality List (JQL) were used as criteria for selecting the highly rated journals. The text-miner software developed was used to crawl and download the abstracts of papers and their bibliometric information from the articles selected from these journal articles. The datasets were transformed into structured data and cleaned using filtering and stemming algorithms. Thereafter, the data were grouped into series of word features based on bag of words document representation. The highly rated journals were clustered using Self-Organising Maps (SOM) method with attribute weights in each cluster.

Keywords:
highly rated journals text mining self-organising maps filtering and stemming algorithms

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