Kamaruddin, Siti Sakira and Abu Bakar, Azuraliza and Hamdan, Abdul Razak and Mat Nor, Fauzias and Ahmad Nazri, Mohd Zakree and Ali Othman, Zulaiha and Hussein, Ghassan Saleh (2015) A text mining system for deviation detection in financial documents. Intelligent Data Analysis, 19 (s1). S19-S44. ISSN 1088467X

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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.

Item Type:	Article
Uncontrolled Keywords:	Deviation detection, text mining, graph-based representation, financial statement analysis, abnormal sentences
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Dr. Siti Sakira Kamaruddin
Date Deposited:	17 Dec 2015 07:10
Last Modified:	27 Apr 2016 04:34
URI:	https://repo.uum.edu.my/id/eprint/16453

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