Document (#38665)

Author

Moohebat, M.
Raj, R.G.
Kareem, S.B.A.
Thorleuchter, D.

Title

Identifying ISI-indexed articles by their lexical usage : a text analysis approach

Source

Journal of the Association for Information Science and Technology. 66(2015) no.3, S.501-511

Year

2015

Abstract

This research creates an architecture for investigating the existence of probable lexical divergences between articles, categorized as Institute for Scientific Information (ISI) and non-ISI, and consequently, if such a difference is discovered, to propose the best available classification method. Based on a collection of ISI- and non-ISI-indexed articles in the areas of business and computer science, three classification models are trained. A sensitivity analysis is applied to demonstrate the impact of words in different syntactical forms on the classification decision. The results demonstrate that the lexical domains of ISI and non-ISI articles are distinguishable by machine learning techniques. Our findings indicate that the support vector machine identifies ISI-indexed articles in both disciplines with higher precision than do the Naïve Bayesian and K-Nearest Neighbors techniques.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23194/abstract.

Theme

Informetrie
Computerlinguistik

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