Affiliations: [a] School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, UUM Sintok, Kedah, Malaysia | [b] Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia | [c] Graduate School of Business, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
Correspondence:
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Corresponding author: Azuraliza Abu Bakar, Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia. Tel.: +60 603 89216794; Fax: +60 603 8921 6184; E-mail:aab@ftsm.ukm.my
Abstract: Attempts to mine text documents to discover deviations or
anomalies have increased in recent years due to the elevated amount of
textual data in today's data repositories. Text mining assists in uncovering
hidden information contents across multiple documents. Although various text
mining tools are available, their focus is mainly to assist in data
summarization or document classification. These tasks proved to be helpful,
however; they do not provide semantic analysis and rigorous textual
comparison to detect abnormal sentences that exist in the documents. In this
paper, we describe a text mining system that is able to detect sentence
deviations from a collection of financial documents. The system implements a
dissimilarity function to compare sentences represented as graphs. Our
evaluation on the proposed system revolves around experiments using
financial statements of a bank. The findings provide valid evidence that the
proposed system is able to identify deviating sentences occurring in the
documents. The detected deviations can be beneficial for the authorities in
order to improve their business decisions.
