An enhanced sequential exception technique for semantic-based text anomaly detection

The detection of semantic-based text anomaly is an interesting research area which has gained considerable attention from the data mining community. Text anomaly detection identifies deviating information from general information contained in documents. Text data are characterized by having problems...

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Main Author:	Taiye, Mohammed Ahmed
Format:	Thesis
Language:	eng eng
Published:	2019
Subjects:	T58.5-58.64 Information technology
Online Access:	https://etd.uum.edu.my/8112/1/s900757_01.pdf https://etd.uum.edu.my/8112/2/s900757_02.pdf
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id	my-uum-etd.8112
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Kamaruddin, Siti Sakira Kabir Ahmad, Farzana
topic	T58.5-58.64 Information technology T58.5-58.64 Information technology
spellingShingle	T58.5-58.64 Information technology T58.5-58.64 Information technology Taiye, Mohammed Ahmed An enhanced sequential exception technique for semantic-based text anomaly detection
description	The detection of semantic-based text anomaly is an interesting research area which has gained considerable attention from the data mining community. Text anomaly detection identifies deviating information from general information contained in documents. Text data are characterized by having problems related to ambiguity, high dimensionality, sparsity and text representation. If these challenges are not properly resolved, identifying semantic-based text anomaly will be less accurate. This study proposes an Enhanced Sequential Exception Technique (ESET) to detect semantic-based text anomaly by achieving five objectives: (1) to modify Sequential Exception Technique (SET) in processing unstructured text; (2) to optimize Cosine Similarity for identifying similar and dissimilar text data; (3) to hybridize modified SET with Latent Semantic Analysis (LSA); (4) to integrate Lesk and Selectional Preference algorithms for disambiguating senses and identifying text canonical form; and (5) to represent semantic-based text anomaly using First Order Logic (FOL) and Concept Network Graph (CNG). ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. Then, FOL and CNG are proposed to represent the detected semantic-based text anomaly. To demonstrate the feasibility of the technique, four selected datasets namely NIPS data, ENRON, Daily Koss blog, and 20Newsgroups were experimented on. The experimental evaluation revealed that ESET has significantly improved the accuracy of detecting semantic-based text anomaly from documents. When compared with existing measures, the experimental results outperformed benchmarked methods with an improved F1-score from all datasets respectively; NIPS data 0.75, ENRON 0.82, Daily Koss blog 0.93 and 20Newsgroups 0.97. The results generated from ESET has proven to be significant and supported a growing notion of semantic-based text anomaly which is increasingly evident in existing literatures. Practically, this study contributes to topic modelling and concept coherence for the purpose of visualizing information, knowledge sharing and optimized decision making.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Taiye, Mohammed Ahmed
author_facet	Taiye, Mohammed Ahmed
author_sort	Taiye, Mohammed Ahmed
title	An enhanced sequential exception technique for semantic-based text anomaly detection
title_short	An enhanced sequential exception technique for semantic-based text anomaly detection
title_full	An enhanced sequential exception technique for semantic-based text anomaly detection
title_fullStr	An enhanced sequential exception technique for semantic-based text anomaly detection
title_full_unstemmed	An enhanced sequential exception technique for semantic-based text anomaly detection
title_sort	enhanced sequential exception technique for semantic-based text anomaly detection
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2019
url	https://etd.uum.edu.my/8112/1/s900757_01.pdf https://etd.uum.edu.my/8112/2/s900757_02.pdf
_version_	1747828328204599296
spelling	my-uum-etd.81122022-05-09T08:13:52Z An enhanced sequential exception technique for semantic-based text anomaly detection 2019 Taiye, Mohammed Ahmed Kamaruddin, Siti Sakira Kabir Ahmad, Farzana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences T58.5-58.64 Information technology QA273-280 Probabilities. Mathematical statistics The detection of semantic-based text anomaly is an interesting research area which has gained considerable attention from the data mining community. Text anomaly detection identifies deviating information from general information contained in documents. Text data are characterized by having problems related to ambiguity, high dimensionality, sparsity and text representation. If these challenges are not properly resolved, identifying semantic-based text anomaly will be less accurate. This study proposes an Enhanced Sequential Exception Technique (ESET) to detect semantic-based text anomaly by achieving five objectives: (1) to modify Sequential Exception Technique (SET) in processing unstructured text; (2) to optimize Cosine Similarity for identifying similar and dissimilar text data; (3) to hybridize modified SET with Latent Semantic Analysis (LSA); (4) to integrate Lesk and Selectional Preference algorithms for disambiguating senses and identifying text canonical form; and (5) to represent semantic-based text anomaly using First Order Logic (FOL) and Concept Network Graph (CNG). ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. Then, FOL and CNG are proposed to represent the detected semantic-based text anomaly. To demonstrate the feasibility of the technique, four selected datasets namely NIPS data, ENRON, Daily Koss blog, and 20Newsgroups were experimented on. The experimental evaluation revealed that ESET has significantly improved the accuracy of detecting semantic-based text anomaly from documents. When compared with existing measures, the experimental results outperformed benchmarked methods with an improved F1-score from all datasets respectively; NIPS data 0.75, ENRON 0.82, Daily Koss blog 0.93 and 20Newsgroups 0.97. The results generated from ESET has proven to be significant and supported a growing notion of semantic-based text anomaly which is increasingly evident in existing literatures. 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